def test_p_roots():
weightf = orth.legendre(5).weight_func
verify_gauss_quad(orth.p_roots, orth.eval_legendre, weightf, -1., 1., 5)
verify_gauss_quad(orth.p_roots,
orth.eval_legendre,
weightf,
-1.,
1.,
25,
atol=1e-13)
verify_gauss_quad(orth.p_roots,
orth.eval_legendre,
weightf,
-1.,
1.,
100,
atol=1e-12)
x, w = orth.p_roots(5, False)
y, v, m = orth.p_roots(5, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
muI, muI_err = integrate.quad(weightf, -1, 1)
assert_allclose(m, muI, rtol=muI_err)
assert_raises(ValueError, orth.p_roots, 0)
assert_raises(ValueError, orth.p_roots, 3.3)
def gauss_quad_2d(func, a, b, n, *args): #Legendre
[x, wx] = p_roots(n[0] + 1)
[y, wy] = p_roots(n[1] + 1)
x, y = np.meshgrid(x, y)
wx, wy = np.meshgrid(wx, wy)
I_G = 0.5*(b[0]-a[0])*0.5*(b[1]-a[1])*np.sum(wx*wy*\
func(0.5*(b[0]-a[0])*x+0.5*(b[0]+a[0]),0.5*(b[1]-a[1])*y+0.5*(b[1]+a[1]),*args))
return I_G
def __init__(self,n1,n2):
self.n1=n1
self.n2=n2
self.xi1,self.w1 = p_roots(n1)
self.xi2,self.w2 = p_roots(n2)
def test_p_roots():
verify_gauss_quad(orth.p_roots, orth.eval_legendre, 5)
verify_gauss_quad(orth.p_roots, orth.eval_legendre, 25, atol=1e-13)
verify_gauss_quad(orth.p_roots, orth.eval_legendre, 100, atol=1e-12)
x, w = orth.p_roots(5, False)
y, v, m = orth.p_roots(5, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
assert_raises(ValueError, orth.p_roots, 0)
assert_raises(ValueError, orth.p_roots, 3.3)
def test_p_roots():
verify_gauss_quad(orth.p_roots, orth.eval_legendre, 5)
verify_gauss_quad(orth.p_roots, orth.eval_legendre, 25, atol=1e-13)
verify_gauss_quad(orth.p_roots, orth.eval_legendre, 100, atol=1e-12)
x, w = orth.p_roots(5, False)
y, v, m = orth.p_roots(5, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
assert_raises(ValueError, orth.p_roots, 0)
assert_raises(ValueError, orth.p_roots, 3.3)
def setup(self, L, U, N, discontinuities=None, **kwargs):
super(GaussQuad, self).setup(L, U, N, **kwargs)
k = self.k
L = self.L
U = self.U
N = self.N
if k:
# compute quad weights and points
if self.qtype == 'GaussLegendre':
from scipy.special.orthogonal import p_roots
xi, w = p_roots(k)
elif self.qtype == 'ClenshawCurtis':
import nodes
xi = np.asarray(map(float, nodes.clenshaw_curtis_nodes(k-1)))
w = np.asarray(map(float, nodes.clenshaw_curtis_weights(k-1)))
# compute cell edges and adjust if necessary
edges = np.linspace(L, U, N+1)
jumps = getattr(self, 'discontinuities', discontinuities)
if self.adjust and jumps:
edges = adjust(edges, jumps)
# compute quad weights and points
x = np.zeros(k*N)
P = np.zeros(k*N)
for i in range(N):
dx2 = 0.5 * (edges[i+1] - edges[i])
for l in range(k):
x[i*k+l] = edges[i] + dx2 * (xi[l] + 1.0)
P[i*k+l] = dx2 * w[l]
self.P = P
self.x = x
else:
# compute quad weights and points
if self.qtype == 'GaussLegendre':
from scipy.special.orthogonal import p_roots
xi, w = p_roots(N)
elif self.qtype == 'ClenshawCurtis':
import nodes
xi = np.asarray(map(float, nodes.clenshaw_curtis_nodes(N-1)))
w = np.asarray(map(float, nodes.clenshaw_curtis_weights(N-1)))
self.P = w * (U - L)/2.0
self.x = (U + L)/2 + (U - L)/2 * xi
def get_quadrature(self, n):
xg, wg = p_roots(n)
xg2, wg2 = p_roots(n + 1)
x = np.zeros((n * (n + 1), 2), dtype=np.double)
w = np.zeros(n * (n + 1), dtype=np.double)
for i in range(n + 1):
for j in range(n):
p = i * n + j
x[p, 0] = xg[j]
x[p, 1] = xg2[i]
w[p] = wg2[i] * wg[j]
return x, w
def gaussian_latitudes(n):
"""
Calculate latitudes of Gaussian grid of n values.
"""
from scipy.special.orthogonal import p_roots
return np.degrees(np.arcsin(np.real(p_roots(n)[0])))
def tminus_longdouble(tmin,tmax,n,norm,root,tp,ntheta=10000,nG=20):
tplus_func=interp1d(np.log(np.longdouble(tp[:,0])/np.longdouble(tmin)),np.longdouble(tp[:,1]))
theta=np.logspace(np.log10(np.longdouble(tmin)),np.log10(np.longdouble(tmax)),ntheta)
#
tminus=np.zeros((ntheta,2))
tminus[:,0]=np.longdouble(theta)
z=np.log(theta/np.longdouble(tmin))
tminus[:,1]=tplus_func(z)
[x,w] = p_roots(nG+1)
integ_limits=np.insert(np.log(root/tmin),0,0)
for iz in range(len(z)):
result=0.
good_integ=(integ_limits<=z[iz])
integ_limits_good=integ_limits[good_integ]
for il in range(1,len(integ_limits_good)):
delta_limit=integ_limits_good[il]-integ_limits_good[il-1]
y_in=0.5*delta_limit*x+0.5*(integ_limits_good[il]+integ_limits_good[il-1])
y=y_in[y_in>=0.]
result+=delta_limit*0.5*sum(w[y_in>=0.]*tminus_integ(y,z[iz],tplus_func))
# print(il)
delta_limit=z[iz]-integ_limits_good[-1]
y_in=x*(delta_limit*0.5)+(z[iz]+integ_limits_good[-1])*0.5
y=y_in[y_in>=0.]
result+=delta_limit*0.5*sum(w[y_in>=0.]*tminus_integ(y,z[iz],tplus_func))
tminus[iz,1]+=result
return tminus
def setQuadrature(self, maxOrder, verbose=False):
super(Uniform, self).setQuadrature(maxOrder)
pts, wts = quads.p_roots(self.quadOrd)
self.pts = self.convertToActual(pts)
for w, wt in enumerate(wts):
wts[w] = wt / (2. * self.range)
self.wts = wts
def _cached_p_roots(n):
if n in _cached_p_roots.cache:
return _cached_p_roots.cache[n]
_cached_p_roots.cache[n] = p_roots(n)
return _cached_p_roots.cache[n]
def test_p_roots():
weightf = orth.legendre(5).weight_func
verify_gauss_quad(orth.p_roots, orth.eval_legendre, weightf, -1.0, 1.0, 5)
verify_gauss_quad(orth.p_roots, orth.eval_legendre, weightf, -1.0, 1.0, 25, atol=1e-13)
verify_gauss_quad(orth.p_roots, orth.eval_legendre, weightf, -1.0, 1.0, 100, atol=1e-12)
x, w = orth.p_roots(5, False)
y, v, m = orth.p_roots(5, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
muI, muI_err = integrate.quad(weightf, -1, 1)
assert_allclose(m, muI, rtol=muI_err)
assert_raises(ValueError, orth.p_roots, 0)
assert_raises(ValueError, orth.p_roots, 3.3)
def gauss1(f, n):
"""gaussian quadrature for weight=1 on (-1,1)"""
[x,w] = p_roots(n+1)
result = 0
for i in range(n+1):
result = result + w[i]*f(x[i])
return result
def gauss(self):
"""
Integrate over the interval 0 to 1. Generate the grid to
evaluate the function on with scipy.special.orthogonal's
p_roots, also generates the weights. p_roots gave the gauss
points in 1d so we make kgrid 3D with itertools.product, then
shift to 0 to 1. Similarly make the weights 3D. Then run the
loops according to haw many bands we are calculating.
"""
self.start = 0
# For even functions do 0.5
self.end = 1.0
self.kgrid, self.weights = p_roots(self.points)
self.kgrid = np.real(self.kgrid)
self.kgrid = np.asarray([x for x in product(self.kgrid, repeat=3)])
self.kgrid = (self.end - self.start) * (self.kgrid +
1) / 2.0 + self.start
self.weights = np.asarray(
[np.product(x) for x in product(self.weights, repeat=3)])
if self.bandnum == 'all':
for num in range(1, len(self.coeffs.keys()) + 1):
self.num = str(num)
self._gaussintegral()
else:
for num in range(1, self.bandnum + 1):
self.num = str(num)
self._gaussintegral()
def setQuadrature(self,maxOrder,verbose=False):
super(Uniform,self).setQuadrature(maxOrder)
pts,wts = quads.p_roots(self.quadOrd)
self.pts=self.convertToActual(pts)
for w,wt in enumerate(wts):
wts[w]=wt/(2.*self.range)
self.wts=wts
def gauss(self):
"""
Integrate over the interval 0 to 1. Generate the grid to
evaluate the function on with scipy.special.orthogonal's
p_roots, also generates the weights. p_roots gave the gauss
points in 1d so we make kgrid 3D with itertools.product, then
shift to 0 to 1. Similarly make the weights 3D. Then run the
loops according to haw many bands we are calculating.
"""
self.start = 0
# For even functions do 0.5
self.end = 1.0
self.kgrid, self.weights = p_roots(self.points)
self.kgrid = np.real(self.kgrid)
self.kgrid = np.asarray([x for x in product(self.kgrid, repeat=3)])
self.kgrid = (self.end-self.start)*(self.kgrid+1)/2.0 + self.start
self.weights = np.asarray([np.product(x) for x in
product(self.weights, repeat=3)])
if self.bandnum == 'all':
for num in range(1, len(self.coeffs.keys())+1):
self.num = str(num)
self._gaussintegral()
else:
for num in range(1, self.bandnum+1):
self.num = str(num)
self._gaussintegral()
def __init__(self, calc, xc='RPA', filename=None,
skip_gamma=False, qsym=True, nlambda=8,
nfrequencies=16, frequency_max=800.0, frequency_scale=2.0,
frequencies=None, weights=None, density_cut=1.e-6,
wcomm=None, chicomm=None, world=mpi.world,
unit_cells=None, tag=None,
txt=sys.stdout):
RPACorrelation.__init__(self, calc, xc=xc, filename=filename,
skip_gamma=skip_gamma, qsym=qsym,
nfrequencies=nfrequencies, nlambda=nlambda,
frequency_max=frequency_max,
frequency_scale=frequency_scale,
frequencies=frequencies, weights=weights,
wcomm=wcomm, chicomm=chicomm, world=world,
txt=txt)
self.l_l, self.weight_l = p_roots(nlambda)
self.l_l = (self.l_l + 1.0) * 0.5
self.weight_l *= 0.5
self.xc = xc
self.density_cut = density_cut
if unit_cells is None:
unit_cells = self.calc.wfs.kd.N_c
self.unit_cells = unit_cells
if tag is None:
tag = self.calc.atoms.get_chemical_formula(mode='hill')
self.tag = tag
def discretizeLegendreBis(self, Axis_i):
x_L = []
w_L = []
if (len(self.listOfBorders[Axis_i]) - 1) != len(
self.listOfNbPointsOnEachIntervalForFinalDiscretization_Axis_k[
Axis_i]):
warnings.warn(
"error",
'nb of extremes must be equal to nb of elements in list of nb of points/sub-interval '
)
else:
for i in range(
len(self.
listOfNbPointsOnEachIntervalForFinalDiscretization_Axis_k[
Axis_i])):
[xi, wi] = p_roots(
self.
listOfNbPointsOnEachIntervalForFinalDiscretization_Axis_k[
Axis_i][i])
coefi = self.getCoefAffine(self.listOfBorders[Axis_i][i],
self.listOfBorders[Axis_i][i + 1],
-1, 1)
xi = self.transformeAffine(xi, coefi)
wi = ((self.listOfBorders[Axis_i][i + 1] -
self.listOfBorders[Axis_i][i]) / 2.0) * wi
x_L = x_L + list(xi)
w_L = w_L + list(wi)
self.listOfTuckerGridNodes_Axis_k[Axis_i] = np.asarray(x_L)
self.weights[Axis_i] = w_L
def test_j_roots():
rf = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
ef = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
wf = lambda a, b: lambda x: (1 - x) ** a * (1 + x) ** b
vgq = verify_gauss_quad
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1.0, 1.0, 5)
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1.0, 1.0, 25, atol=1e-12)
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1.0, 1.0, 100, atol=1e-11)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1.0, 1.0, 5)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1.0, 1.0, 25, atol=1.5e-13)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1.0, 1.0, 100, atol=1e-12)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1.0, 1.0, 5, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1.0, 1.0, 25, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1.0, 1.0, 100, atol=1e-12)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1.0, 1.0, 5)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1.0, 1.0, 25, atol=1e-13)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1.0, 1.0, 100, atol=2e-13)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1.0, 1.0, 5)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1.0, 1.0, 25)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1.0, 1.0, 100, atol=1e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1.0, 1.0, 5, atol=1e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1.0, 1.0, 25, atol=2e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1.0, 1.0, 100, atol=1e-11)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1.0, 1.0, 5)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1.0, 1.0, 25, atol=1e-13)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1.0, 1.0, 100, atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
muI, muI_err = integrate.quad(wf(2, 3), -1, 1)
assert_allclose(m, muI, rtol=muI_err)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def gauss(f, a, b, n):
"""gaussian quadrature for weight=1 on (a,b)"""
[x,w] = p_roots(n+1)
result = 0
for i in range(n+1):
temp = (b-a)*x[i]/2 + (b+a)/2
result = result + w[i]*f(temp)
return (b-a)*result/2
def gaussian_quadrature(func, n, a, b):
""" Integrates a function using the Gaussian quadratures of n nodes
with accuracy O((b-a)^2*n). """
x, w = p_roots(n + 1)
G = 0.5 * (b - a) * sum(w * func(0.5 * (b - a) * x + 0.5 * (b + a)))
return G
def gauss_contour(vertices, points_per_segment):
"""
On the line connecting each pair of vertices, generates
points and weights using Gauss-Legendre quadrature.
"""
segment_count = len(vertices) - 1
points = weights = sp.empty(0, complex)
for i in range(segment_count):
try: (x, w) = p_roots(points_per_segment[i])
except: (x, w) = p_roots(points_per_segment)
a = vertices[i]
b = vertices[i + 1]
scaled_x = (x * (b - a) + (a + b))/2
scaled_w = w * (b - a)/2
points = sp.hstack((points, scaled_x))
weights = sp.hstack((weights, scaled_w))
return (points, weights)
def setQuadrature(self,maxOrder,verbose=False):
super(Uniform,self).setQuadrature(maxOrder)
#standard Legendre quadrature
pts,wts = quads.p_roots(self.quadOrd)
self.pts,self.wts,self.quadOrds=super(Uniform,self).checkPoints(pts,wts)
self.quaddict = {}
for o,order in enumerate(self.quadOrds):
self.quaddict[order]=(self.pts[o],self.wts[o])
def gl_nodes(npts, a, b):
"""
Return the nodes for `npts`-point Gauss-Legendre quadrature over the
interval [a, b].
"""
# The default definition is over [-1, 1]; shift and scale.
nodes, wts = p_roots(npts)
# return 0.5*(nodes + 1.)
return a + 0.5 * (b - a) * (nodes + 1.)
def _cached_p_roots(n):
"""
Cache p_roots results to speed up calls of the fixed_quad function.
"""
if n in _cached_p_roots.cache:
return _cached_p_roots.cache[n]
_cached_p_roots.cache[n] = p_roots(n)
return _cached_p_roots.cache[n]
def setQuadrature(self, maxOrder, verbose=False):
super(Uniform, self).setQuadrature(maxOrder)
#standard Legendre quadrature
pts, wts = quads.p_roots(self.quadOrd)
self.pts, self.wts, self.quadOrds = super(Uniform,
self).checkPoints(pts, wts)
self.quaddict = {}
for o, order in enumerate(self.quadOrds):
self.quaddict[order] = (self.pts[o], self.wts[o])
def _cached_p_roots(n):
"""
Cache p_roots results for speeding up multiple calls of the fixed_quad function.
"""
if n in _cached_p_roots.cache:
return _cached_p_roots.cache[n]
_cached_p_roots.cache[n] = p_roots(n)
return _cached_p_roots.cache[n]
def _cached_p_roots(n):
"""
Cache p_roots results to speed up calls of the fixed_quad function.
"""
if n in _cached_p_roots.cache:
return _cached_p_roots.cache[n]
_cached_p_roots.cache[n] = p_roots(n)
return _cached_p_roots.cache[n]
def _cached_p_roots(n):
"""
Cache p_roots results for speeding up multiple calls of the fixed_quad function.
"""
if n in _cached_p_roots.cache:
return _cached_p_roots.cache[n]
_cached_p_roots.cache[n] = p_roots(n)
return _cached_p_roots.cache[n]
def gl_nodes(npts, a, b):
"""
Return the nodes for `npts`-point Gauss-Legendre quadrature over the
interval [a, b].
"""
# The default definition is over [-1, 1]; shift and scale.
nodes, wts = p_roots(npts)
# return 0.5*(nodes + 1.)
return a + 0.5*(b - a)*(nodes + 1.)
def gauss_nodes(n, a, b):
"""
Get Gaussian Legraend nodes and weights
"""
[x, w] = p_roots(n)
x = (b - a) * x + (b + a)
x /= 2
w *= 0.5 * (b - a)
return x, w
def get_gauss_legendre_points(nw=16, frequency_max=800.0, frequency_scale=2.0):
y_w, weights_w = p_roots(nw)
ys = 0.5 - 0.5 * y_w
ys = ys[::-1]
w = (-np.log(1 - ys))**frequency_scale
w *= frequency_max / w[-1]
alpha = (-np.log(1 - ys[-1]))**frequency_scale / frequency_max
transform = (-np.log(1 - ys))**(frequency_scale - 1) \
/ (1 - ys) * frequency_scale / alpha
return w, weights_w * transform / 2
def discretizeLegendre(self, Axis_i):
[xi, self.weights[Axis_i]] = p_roots(
self.
listOfNbPointsOnEachIntervalForFinalDiscretization_Axis_kTotal[
Axis_i])
self.getDxiAfterTransf(
xi, Axis_i) # Update self.listOfTuckerGridNodes_Axis_k[Axis_i]
self.weights[Axis_i] = (self.lx[Axis_i] / 2) * self.weights[Axis_i]
def gauss_quadratur(xs,a,b,f):
[x,w] = p_roots(len(xs))
summe = 0.0
#print len(x)
for i in range (0,len(x)):
xu = (b-a)/2 * x[i] + (a+b)/2
summe = summe + (w[i] * f(xu))
return (b-a)/2 * summe
"""
def setQuadrature(self, inputfile=None, order=2, verbose=False):
if verbose:
print 'set quadrature for', self.name
#get order from input file
if inputfile != None:
self.order = inputfile('Variables/' + self.name + '/order', 2)
else:
self.order = order
#standard legendre quadrature
self.pts, self.wts = quads.p_roots(self.order)
def setQuadrature(self,inputfile=None,order=2,verbose=False):
if verbose:
print 'set quadrature for',self.name
#get order from input file
if inputfile != None:
self.order=inputfile('Variables/'+self.name+'/order',2)
else:
self.order=order
#standard legendre quadrature
self.pts,self.wts = quads.p_roots(self.order)
def test_j_roots():
roots = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
evalf = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 5)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 25, atol=1e-12)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 100, atol=1e-11)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 5)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 25, atol=1e-13)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 100, atol=1e-12)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 5, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 25, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 100, atol=1e-12)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 5)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 25, atol=1e-13)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 100, atol=2e-13)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 5)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 25)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 100, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 5, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 25, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 100, atol=1e-11)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 5)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 25, atol=1e-13)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 100, atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def fixed_quad(func, a, b, n, args=()):
[x, w] = p_roots(n)
x = real(x)
#y = (b-a)*(x+1)/2.0 + a
sum_integral = 0
i = 0
for xi in x:
sum_integral = sum_integral + w[i] * func(
(b - a) * (xi + 1) / 2.0 + a, *args)
i = i + 1
return (b - a) / 2.0 * sum_integral
def get_gauss_legendre_points(nw=16, frequency_max=800.0, frequency_scale=2.0):
y_w, weights_w = p_roots(nw)
y_w = y_w.real
ys = 0.5 - 0.5 * y_w
ys = ys[::-1]
w = (-np.log(1 - ys))**frequency_scale
w *= frequency_max / w[-1]
alpha = (-np.log(1 - ys[-1]))**frequency_scale / frequency_max
transform = (-np.log(1 - ys))**(frequency_scale - 1) \
/ (1 - ys) * frequency_scale / alpha
return w, weights_w * transform / 2
def fixed_quad_me(func, a, b, args=(), n=5):
"""
My version of fixed quad which will then be able to sum over NaNs.
"""
[x,w] = p_roots(n)
x = real(x)
ainf, binf = map(isinf, (a,b))
if ainf or binf:
raise ValueError("Gaussian quadrature is only avalible for finite limits.")
y = (b-a)*(x+1)/2.0 + a
return (b-a)/2.0*np.nansum(w*func(y,*args),0), None
def test_j_roots():
roots = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
evalf = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 5)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 25, atol=1e-12)
verify_gauss_quad(roots(-0.5, -0.75), evalf(-0.5, -0.75), 100, atol=1e-11)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 5)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 25, atol=1e-13)
verify_gauss_quad(roots(0.5, -0.5), evalf(0.5, -0.5), 100, atol=1e-12)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 5, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 25, atol=2e-13)
verify_gauss_quad(roots(1, 0.5), evalf(1, 0.5), 100, atol=1e-12)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 5)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 25, atol=1e-13)
verify_gauss_quad(roots(0.9, 2), evalf(0.9, 2), 100, atol=2e-13)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 5)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 25)
verify_gauss_quad(roots(18.24, 27.3), evalf(18.24, 27.3), 100, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 5, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 25, atol=1e-13)
verify_gauss_quad(roots(47.1, -0.2), evalf(47.1, -0.2), 100, atol=1e-11)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 5)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 25, atol=1e-13)
verify_gauss_quad(roots(2.25, 68.9), evalf(2.25, 68.9), 100, atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def ft(self, x):
x = x * self._supp * np.pi
nroots = 2 * self._supp
if self._supp % 2 == 0:
nroots += 1
q, weights = p_roots(nroots)
ind = q > 0
weights = weights[ind]
q = q[ind]
kq = np.outer(x, q) if len(x.shape) == 1 else np.einsum(
'ij,k->ijk', x, q)
arr = np.sum(weights * self._raw(q) * np.cos(kq), axis=-1)
return self._supp * arr
def quad_data(n, interval):
from scipy.special.orthogonal import p_roots
a, b = interval
# transform gauss quadrature data
midpoint = (a+b)/2
length = b-a
orig_particles, orig_gauss_weights = p_roots(n)
particles = [midpoint + x*length/2 for x in orig_particles]
gauss_weights = length/2*orig_gauss_weights
return particles, gauss_weights
def quad_data(n, interval):
from scipy.special.orthogonal import p_roots
a, b = interval
# transform gauss quadrature data
midpoint = (a + b) / 2
length = b - a
orig_particles, orig_gauss_weights = p_roots(n)
particles = [midpoint + x * length / 2 for x in orig_particles]
gauss_weights = length / 2 * orig_gauss_weights
return particles, gauss_weights
def rule(n,a=0.,b=1.,kind='legendre'):
# if table entry does not exist, generate it
try:
x,w=quad_table[kind+'_'+str(n)]
except:
if kind=='legendre': x,w=so.p_roots(n)
else: sys.exit('undefined quadrature kind "'+kind+'"')
# append to table
quad_table.setdefault[kind+'_'+str(n),(x,w)]
# return shifted and scaled rule
return a+x*(b-a),w*(b-a)
def get_quadrature(self, n):
xg, wg = p_roots(n)
x = np.zeros((n ** 3, 3), dtype=np.double)
w = np.zeros(n ** 3, dtype=np.double)
for i in range(n):
for j in range(n):
for k in range(n):
p = i * n * n + j * n + k
x[p, 0] = xg[k]
x[p, 1] = xg[j]
x[p, 2] = xg[i]
w[p] = wg[i] * wg[j] * wg[k]
return x, w
def gauss_contour(vertices, order):
"""
Generates a contour using Gauss-Legendre quadrature.
"""
(x, w) = p_roots(order)
num_segments = len(vertices) - 1
points = weights = sp.empty(0, complex)
for i in range(num_segments):
a = vertices[i]
b = vertices[i + 1]
scaled_x = (x * (b - a) + (a + b))/2
scaled_w = w * (b - a)/2
points = sp.hstack((points, scaled_x))
weights = sp.hstack((weights, scaled_w))
return (points, weights)
def fixed_quad(func,a,b,args=(),n=5):
"""
Compute a definite integral using fixed-order Gaussian quadrature.
Integrate `func` from `a` to `b` using Gaussian quadrature of
order `n`.
Parameters
----------
func : callable
A Python function or method to integrate (must accept vector inputs).
a : float
Lower limit of integration.
b : float
Upper limit of integration.
args : tuple, optional
Extra arguments to pass to function, if any.
n : int, optional
Order of quadrature integration. Default is 5.
Returns
-------
val : float
Gaussian quadrature approximation to the integral
See Also
--------
quad : adaptive quadrature using QUADPACK
dblquad : double integrals
tplquad : triple integrals
romberg : adaptive Romberg quadrature
quadrature : adaptive Gaussian quadrature
romb : integrators for sampled data
simps : integrators for sampled data
cumtrapz : cumulative integration for sampled data
ode : ODE integrator
odeint : ODE integrator
"""
[x,w] = p_roots(n)
x = real(x)
ainf, binf = map(isinf,(a,b))
if ainf or binf:
raise ValueError("Gaussian quadrature is only available for "
"finite limits.")
y = (b-a)*(x+1)/2.0 + a
return (b-a)/2.0*sum(w*func(y,*args),0), None
def __init__(self, k=None, qtype='GaussLegendre'):
if k:
if qtype == 'GaussLegendre':
name = 'GENClarkGL(' + str(k) + ')'
from scipy.special.orthogonal import p_roots
self.xi, self.w = p_roots(k)
elif qtype == 'ClenshawCurtis':
name = 'GENClarkCC(' + str(k) + ')'
import nodes
self.xi = np.asarray(map(float, nodes.clenshaw_curtis_nodes(k-1)))
self.w = np.asarray(map(float, nodes.clenshaw_curtis_weights(k-1)))
else:
name = 'GENClarkQP'
self.k = k
self.name = name
def __init__(self,
calc,
xc='RPA',
filename=None,
skip_gamma=False,
qsym=True,
nlambda=8,
nfrequencies=16,
frequency_max=800.0,
frequency_scale=2.0,
frequencies=None,
weights=None,
density_cut=1.e-6,
world=mpi.world,
nblocks=1,
unit_cells=None,
tag=None,
txt=sys.stdout):
RPACorrelation.__init__(self,
calc,
xc=xc,
filename=filename,
skip_gamma=skip_gamma,
qsym=qsym,
nfrequencies=nfrequencies,
nlambda=nlambda,
frequency_max=frequency_max,
frequency_scale=frequency_scale,
frequencies=frequencies,
weights=weights,
world=world,
nblocks=nblocks,
txt=txt)
self.l_l, self.weight_l = p_roots(nlambda)
self.l_l = (self.l_l + 1.0) * 0.5
self.weight_l *= 0.5
self.xc = xc
self.density_cut = density_cut
if unit_cells is None:
unit_cells = self.calc.wfs.kd.N_c
self.unit_cells = unit_cells
if tag is None:
tag = self.calc.atoms.get_chemical_formula(mode='hill')
self.tag = tag
def quad01(n,kind='gauss-legendre'):
"""get n-point quadrature rules on [0,1] and keep them in table
"""
try:
x,w=quad01_table[kind+'_'+str(n)]
except:
# if table entry does not exist, generate it
if kind=='gauss-legendre':
x,w=so.p_roots(n)
x=(x+1)/2
w=w/2
else:
sys.exit('undefined quadrature kind="'+kind+'"')
quad01_table[kind+'_'+str(n)]=(x,w)
return x,w
def integrate(self, n, m=None):
# for simplicity, I'm not going to implement subitervals
# I'm pretty sure with Guassian Quadrature it doesn't make sense to
# and you will get a better approximation using the full function
"""
if m is None:
m = self.N
subintervals = self._get_subintervals(m)
"""
# I couldn't figure out how to calculate the quadrature weights manually
# so we cheat and use scipy
[X, A] = p_roots(n)
if self.verbose:
print("X:", X)
print("A:", A)
print()
# The above assume (a, b) = (-1, 1)
# we do a change of variables to convert them to any interval
scale, shift = self.change_of_variables(self.a, self.b)
X = [shift + scale * x for x in X]
A = [scale * Ai for Ai in A]
if self.verbose:
print("CoV X:", X)
print("CoV A:", A)
print()
# I add these only so the printing looks better
#X = [self.a] + X + [self.b]
#A = [0] + A + [0]
# reinitializing everything so our functions work
super(GaussianQuadrature, self).__init__(X,
self.f,
var=str(self.var),
verbose=self.verbose)
# calling our generic quadrature integration approximation
return super(GaussianQuadrature, self).integrate(A=A)
def calculate_error_l2_norm(self, dY):
"""
Returns the L2 norm of the vector dY.
E.g. the square root of the sum of squares of the components of dY.
"""
solutions = []
norm = 0.
for mi in range(len(self._meshes)):
for ei in range(len(self._meshes[mi].elements)):
e = self._meshes[mi].elements[ei]
# change this to gauss points:
x_vals, w = p_roots(20)
norm_e_squared = 0.
for i, x in enumerate(x_vals):
norm_e_squared += w[i] * \
self.get_sol_value(mi, ei, dY, x,
count_lift=False)**2
norm_e_squared *= e.jacobian
norm += norm_e_squared
return sqrt(norm)
def fixed_quad(func,a,b,args=(),n=5):
"""Compute a definite integral using fixed-order Gaussian quadrature.
Description:
Integrate func from a to b using Gaussian quadrature of order n.
Inputs:
func -- a Python function or method to integrate
(must accept vector inputs)
a -- lower limit of integration
b -- upper limit of integration
args -- extra arguments to pass to function.
n -- order of quadrature integration.
Outputs: (val, None)
val -- Gaussian quadrature approximation to the integral.
See also:
quad - adaptive quadrature using QUADPACK
dblquad, tplquad - double and triple integrals
romberg - adaptive Romberg quadrature
quadrature - adaptive Gaussian quadrature
romb, simps, trapz - integrators for sampled data
cumtrapz - cumulative integration for sampled data
ode, odeint - ODE integrators
"""
[x,w] = p_roots(n)
x = real(x)
ainf, binf = map(isinf,(a,b))
if ainf or binf:
raise ValueError, "Gaussian quadrature is only available for " \
"finite limits."
y = (b-a)*(x+1)/2.0 + a
return (b-a)/2.0*sum(w*func(y,*args),0), None
def fixed_quad(func, a, b, args=(), n=5):
"""Compute a definite integral using fixed-order Gaussian quadrature.
Description:
Integrate func from a to b using Gaussian quadrature of order n.
Inputs:
func -- a Python function or method to integrate
(must accept vector inputs)
a -- lower limit of integration
b -- upper limit of integration
args -- extra arguments to pass to function.
n -- order of quadrature integration.
Outputs: (val, None)
val -- Gaussian quadrature approximation to the integral.
See also:
quad - adaptive quadrature using QUADPACK
dblquad, tplquad - double and triple integrals
romberg - adaptive Romberg quadrature
quadrature - adaptive Gaussian quadrature
romb, simps, trapz - integrators for sampled data
cumtrapz - cumulative integration for sampled data
ode, odeint - ODE integrators
"""
[x, w] = p_roots(n)
x = real(x)
ainf, binf = map(isinf, (a, b))
if ainf or binf:
raise ValueError, "Gaussian quadrature is only available for " \
"finite limits."
y = (b - a) * (x + 1) / 2.0 + a
return (b - a) / 2.0 * sum(w * func(y, *args), 0), None
def test_j_roots():
rf = lambda a, b: lambda n, mu: orth.j_roots(n, a, b, mu)
ef = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
wf = lambda a, b: lambda x: (1 - x)**a * (1 + x)**b
vgq = verify_gauss_quad
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1., 5)
vgq(rf(-0.5, -0.75),
ef(-0.5, -0.75),
wf(-0.5, -0.75),
-1.,
1.,
25,
atol=1e-12)
vgq(rf(-0.5, -0.75),
ef(-0.5, -0.75),
wf(-0.5, -0.75),
-1.,
1.,
100,
atol=1e-11)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 5)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 25, atol=1.5e-13)
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 100, atol=1e-12)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 5, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 25, atol=2e-13)
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 100, atol=1e-12)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 5)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 25, atol=1e-13)
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 100, atol=2e-13)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 5)
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 25)
vgq(rf(18.24, 27.3),
ef(18.24, 27.3),
wf(18.24, 27.3),
-1.,
1.,
100,
atol=1e-13)
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1., 5, atol=1e-13)
vgq(rf(47.1, -0.2),
ef(47.1, -0.2),
wf(47.1, -0.2),
-1.,
1.,
25,
atol=2e-13)
vgq(rf(47.1, -0.2),
ef(47.1, -0.2),
wf(47.1, -0.2),
-1.,
1.,
100,
atol=1e-11)
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1., 5)
vgq(rf(2.25, 68.9),
ef(2.25, 68.9),
wf(2.25, 68.9),
-1.,
1.,
25,
atol=1e-13)
vgq(rf(2.25, 68.9),
ef(2.25, 68.9),
wf(2.25, 68.9),
-1.,
1.,
100,
atol=1e-13)
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
xj, wj = orth.j_roots(6, 0.0, 0.0)
xl, wl = orth.p_roots(6)
assert_allclose(xj, xl, 1e-14, 1e-14)
assert_allclose(wj, wl, 1e-14, 1e-14)
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
xj, wj = orth.j_roots(6, 4.0, 4.0)
xc, wc = orth.cg_roots(6, 4.5)
assert_allclose(xj, xc, 1e-14, 1e-14)
assert_allclose(wj, wc, 1e-14, 1e-14)
x, w = orth.j_roots(5, 2, 3, False)
y, v, m = orth.j_roots(5, 2, 3, True)
assert_allclose(x, y, 1e-14, 1e-14)
assert_allclose(w, v, 1e-14, 1e-14)
muI, muI_err = integrate.quad(wf(2, 3), -1, 1)
assert_allclose(m, muI, rtol=muI_err)
assert_raises(ValueError, orth.j_roots, 0, 1, 1)
assert_raises(ValueError, orth.j_roots, 3.3, 1, 1)
assert_raises(ValueError, orth.j_roots, 3, -2, 1)
assert_raises(ValueError, orth.j_roots, 3, 1, -2)
assert_raises(ValueError, orth.j_roots, 3, -2, -2)
def Telles(self, pxi1, pxi2, e, mesh, k):
# Collocation point at (pxi1,pxi2)
# See research log pg. 789
xy = np.array((
[-1, 1, -1, -1], # T1
[1, 1, -1, 1], # T2
[1, -1, 1, 1], # T3
[-1, -1, 1, -1])) # T4
# Ftheta function
def F(i, theta):
if i == 0:
return (-1 - pxi2) / np.sin(theta)
elif i == 1:
return (1 - pxi1) / np.cos(theta)
elif i == 2:
return (1 - pxi2) / np.sin(theta)
elif i == 3:
return (-1 - pxi1) / np.cos(theta)
# Which triangles to calculate
which = np.array([False, False, False, False])
if pxi2 > -1:
which[0] = True
if pxi1 < 1:
which[1] = True
if pxi2 < 1:
which[2] = True
if pxi1 > -1:
which[3] = True
xi1 = []
xi2 = []
w1 = []
w2 = []
J = []
for i in xrange(4):
if which[i]:
x1, x2, y1, y2 = xy[i]
theta1 = np.arctan2(y1 - pxi2, x1 - pxi1)
theta2 = np.arctan2(y2 - pxi2, x2 - pxi1)
if theta1 < 0: theta1 += 2 * np.pi
if i == 1 and theta1 > 0: theta1 -= 2 * np.pi
if theta2 < 0: theta2 += 2 * np.pi
theta, wtheta = p_roots(self.n1)
theta = theta * 0.5 * (theta2 - theta1) + 0.5 * (theta1 +
theta2)
wtheta = wtheta * 0.5 * (theta2 - theta1)
if e.num_integration_cells > 1:
s = np.arange(0, e.num_integration_cells).reshape(-1, 1)
theta = (
(theta - theta1 +
(theta2 - theta1) * s) / e.num_integration_cells +
theta1).reshape(-1, )
wtheta = np.repeat([wtheta / e.num_integration_cells],
e.num_integration_cells,
axis=0).reshape(-1, )
Ftheta = F(i, theta)
theta = (np.repeat(theta, self.n2))
wtheta = (np.repeat(wtheta, self.n2))
rho, wrho = p_roots(self.n2)
if e.num_integration_cells > 1:
rho = ((rho - 1 + 2 * s) / e.num_integration_cells +
1).reshape(-1, )
wrho = np.repeat([wrho / e.num_integration_cells],
e.num_integration_cells,
axis=0).reshape(-1, )
rho = np.vstack([rho * 0.5 * Ftheta[i] + 0.5 * Ftheta[i]]
for i in xrange(self.n1)).reshape(-1, )
wrho = np.vstack([wrho * 0.5 * Ftheta[i]]
for i in xrange(self.n1)).reshape(-1, )
xi1.append(rho * np.cos(theta) + pxi1)
xi2.append(rho * np.sin(theta) + pxi2)
w1.append(wtheta)
w2.append(wrho)
J.append(rho)
xi1 = np.array(xi1).reshape(-1, )
xi2 = np.array(xi2).reshape(-1, )
w1 = np.array(w1).reshape(-1, )
w2 = np.array(w2).reshape(-1, )
J = np.array(J).reshape(-1, )
return xi1, xi2, w1 * J, w2 * J