import pyJvsip as pjv
from math import pi as pi
view = pjv.create
L = 20 # A length
a = view('vview_d', L).ramp(0.0, 2.0 * pi / float(L - 1))
b = a.empty
ab_bl = view('vview_bl', L)
pjv.cos(a, b)
a.fill(0.0)
pjv.lgt(b, a, ab_bl)
assert ab_bl.anytrue, 'No true values in boolean vector'
ab_vi = ab_bl.indexbool
pjv.gather(b, ab_vi, a.putlength(ab_vi.length))
print(repr([(ab_vi[i], a[i]) for i in range(ab_vi.length)]))
#! python
# Estimate two-norm (sigma0) and singular values (sigma0, sigma1)
# of a two by two matrix by searching through a dense space of vectors.
# Check by reproducing estimate for A matrix using Aest=U Sigma V^t
# Motivating text Numerical Linear Algebra, Trefethen and Bau
import pyJvsip as pv
from numpy import pi,arccos
from matplotlib.pyplot import *
N=1000 #number of points to search through for estimates
# create a vector 'arg' of evenly spaced angles between [zero, 2 pi)
arg=pv.create('vview_d',N).ramp(0.0,2.0 * pi/float(N))
# Create a matrix 'X' of unit vectors representing Unit Ball in R2
X=pv.create('mview_d',2,N,'ROW').fill(0.0)
x0=X.rowview(0);x1=X.rowview(1)
pv.sin(arg,x0);pv.cos(arg,x1)
# create matrix 'A' to examine
A=pv.create('mview_d',2,2).fill(0.0)
A[0,0]=1.0;A[0,1]=2.0; A[1,1]=2.0;A[1,0]=0.0
# create matrix Y=AX
Y=A.prod(X)
y0=Y.rowview(0);y1=Y.rowview(1)
# create vector 'n' of 2-norms for Y col vectors (sqrt(y0_i^2+y1_i^2))
n=(y0.copy.sq+y1.copy.sq).sqrt
# Find index of (first) maximum value and (first) minimum value
i=n.maxvalindx; j=n.minvalindx
sigma0=n[i];sigma1=n[j]
# create estimates for V, U, Sigma (S)
V=A.empty.fill(0)
U=V.copy
S=V.copy
S[0,0]=sigma0;S[1,1]=sigma1
import pyJvsip as pjv
from math import pi as pi
view=pjv.create
L = 20 # A length
a = view('vview_d',L).ramp(0.0,2.0*pi/float(L-1))
b = a.empty
ab_bl=view('vview_bl',L)
pjv.cos(a,b)
a.fill(0.0)
pjv.lgt(b,a,ab_bl)
assert ab_bl.anytrue, 'No true values in boolean vector'
ab_vi = ab_bl.indexbool
pjv.gather(b,ab_vi,a.putlength(ab_vi.length))
print(repr([(ab_vi[i], a[i]) for i in range(ab_vi.length)]))
import pyJvsip as pjv
from math import pi as pi
n = 1000
x = pjv.create('vview_d', n).ramp(0, 2 * pi / float(n))
c = pjv.cos(x, x.empty)
cClip = pjv.clip(c, -.9, .9, -.8, .8, c.empty)
import pyJvsip as pjv
from math import pi as pi
n=1000
x=pjv.create('vview_d',n).ramp(0,2*pi/float(n))
c=pjv.cos(x,x.empty)
cinvClip=pjv.invclip(c,-.6,0.0,.6,-.7,.7,c.empty)
# Estimate two-norm (sigma0) and singular values (sigma0, sigma1)
# of a two by two matrix by searching through a dense space of vectors.
# Check by reproducing estimate for A matrix using Aest=U Sigma V^t
# Motivating text Numerical Linear Algebra, Trefethen and Bau
import pyJvsip as pv
from numpy import pi, arccos
from matplotlib.pyplot import *
N = 1000 #number of points to search through for estimates
# create a vector 'arg' of evenly spaced angles between [zero, 2 pi)
arg = pv.create('vview_d', N).ramp(0.0, 2.0 * pi / float(N))
# Create a matrix 'X' of unit vectors representing Unit Ball in R2
X = pv.create('mview_d', 2, N, 'ROW').fill(0.0)
x0 = X.rowview(0)
x1 = X.rowview(1)
pv.sin(arg, x0)
pv.cos(arg, x1)
# create matrix 'A' to examine
A = pv.create('mview_d', 2, 2).fill(0.0)
A[0, 0] = 1.0
A[0, 1] = 2.0
A[1, 1] = 2.0
A[1, 0] = 0.0
# create matrix Y=AX
Y = A.prod(X)
y0 = Y.rowview(0)
y1 = Y.rowview(1)
# create vector 'n' of 2-norms for Y col vectors (sqrt(y0_i^2+y1_i^2))
n = (y0.copy.sq + y1.copy.sq).sqrt
# Find index of (first) maximum value and (first) minimum value
i = n.maxvalindx
j = n.minvalindx
