def testjac(dpsv, nv, av, bv, epsv):
print ">>>>>> r_jacobi(n,a,b)"
print "{0:>4s} {1:>4s} {2:>4s} {3:>4s} {4:>10s} {5:>15s} {6:>10s} {7:>10s}".\
format("n", "a", "b", "dps", "eps", "errmaxnorm", "tg", "ts")
for ni,ai,bi,dpsi,epsi in product(nv,av,bv,dpsv,epsv):
mp.dps = dpsi
# Compute using the closed form
ab = r_jacobi(ni,ai,bi)
# Calculate Gauss quadrature rules
t1 = time.time()
xw = gauss(ab)
t2 = time.time()
# Invoke the Stieltjes algorithm to calculate ab again
t3 = time.time()
ab1 = dsti(xw)
t4 = time.time()
err = norm(ab-ab1, inf)
print "{0:>4d} {1:>4s} {2:>4s} {3:>4d} {4:>10s} {5:>15s} {6:>10f} {7:>10f}".format(\
ni, nstr(ai), nstr(bi), dpsi, nstr(epsi), nstr(err), t2-t1, t4-t3)
def randomize_time(mean):
allowed_range = mean * STDEV
stdev = allowed_range / 3 # 99.73% chance to be in the allowed range
t = 0
while abs(mean - t) > allowed_range:
t = gauss(mean, stdev)
return t
def _do_inversion(com, rws):
"""
Print formatting messages and call gauss elimination
:param com: communicator
:param rws: rows
:return: inversed rows
"""
print(formatting(com.rank, format_action('receive', rows=rws)))
inv = gauss(np.array(rws, dtype=np.float64), com, n)
print(formatting(com.rank, format_action('inverse', rows=inv)))
return inv
def polynomialInterpolation(x1, x2, x3, dx0, dx1):
'''Given three one dimension points and the curve's derivative vectors of start and end points,
calculate a four order polynomial curve(x = n4*t^4 + n3*t^3 + n2*t^2 + n1*t + n0) based on t = [0, 0.5, 1],
[x1, x2, x3] <=> t=[0, 0.5, 1]
[dx0, dx1] <=> t = [0, 1]
return polynomial coefficients c = [n4, n3, n2, n1, n0]
'''
b = np.array([x3, x2, dx1, dx0, x1], dtype=np.float)
#np.array() <=> np.matrix()
A = np.array( [[1, 1, 1, 1, 1],\
[0.5**4, 0.5**3, 0.5**2, 0.5, 1],\
[4, 3, 2, 1, 0],\
[0, 0, 0, 1, 0],\
[0, 0, 0, 0, 1]], dtype = np.float)
x = gauss(A, b)
return x
print ">>>> gauss"
dpsv = [5, 15, 50]
nv = [10, 20]
av = [mpf(1)]
bv = [mpf(1)]
epsv = [mpf(1e-5), mpf(1e-15), mpf(1e-50)]
#testjac(dpsv,nv,av,bv,epsv)
n=5
kappa = 1
ab = r_jacobi(n,0,0)
p = ab_to_poly(ab)
xw = gauss(ab)
els = uniform_els(-1,1,3)
print els
fea_diri2(els, kappa, 0, xw)
quit()
from pylab import *
xx = arange(-1.0, 1.0, 0.01)
for i in range(n):
plot(xx,polyvalv(p[i,:],xx))
axis([-1,1,-0.5,0.5])
grid(True)
show()
def lognormal(x, mean, sigma):
l = log10(x)
mean = log10(mean)
val = gauss(l, mean, sigma)
return val
#!usr/bin/env python from auxFuncSLE import * from gauss import * from gaussJordan import * piv = 2 A = [[2.11, -4.21, 0.921], [4.01, 10.2, -1.12], [1.09, 0.987, 0.832]] b = [2.01, -3.09, 4.21] # gauss print "\nMetodo Gauss Jordan" X = gauss(A, b, piv) print X # gauss Jordan print "\nMetodo Gauss Jordan" X = gaussJordan(A, b, piv) print X print
def gauss_main():
matrix = [[0.10, 12, -0.13, 0.10], [0.12, 0.71, 0.15, 0.26],
[-0.13, 0.15, 0.63, 0.38]]
x = gauss(3, matrix)
print(x)
