def test_eig_dyn():
v = 0
for i in xrange(5):
n = 1 + int(mp.rand() * 5)
if mp.rand() > 0.5:
# real
A = 2 * mp.randmatrix(n, n) - 1
if mp.rand() > 0.5:
A *= 10
for x in xrange(n):
for y in xrange(n):
A[x, y] = int(A[x, y])
else:
A = (2 * mp.randmatrix(n, n) -
1) + 1j * (2 * mp.randmatrix(n, n) - 1)
if mp.rand() > 0.5:
A *= 10
for x in xrange(n):
for y in xrange(n):
A[x,
y] = int(mp.re(A[x, y])) + 1j * int(mp.im(A[x, y]))
run_hessenberg(A, verbose=v)
run_schur(A, verbose=v)
run_eig(A, verbose=v)
def test_svd_c_rand():
for i in xrange(5):
full = mp.rand() > 0.5
m = 1 + int(mp.rand() * 10)
n = 1 + int(mp.rand() * 10)
A = (2 * mp.randmatrix(m, n) - 1) + 1j * (2 * mp.randmatrix(m, n) - 1)
if mp.rand() > 0.5:
A *= 10
for x in xrange(m):
for y in xrange(n):
A[x, y] = int(mp.re(A[x, y])) + 1j * int(mp.im(A[x, y]))
run_svd_c(A, full_matrices=full, verbose=False)
def test_svd_c_rand():
for i in xrange(5):
full = mp.rand() > 0.5
m = 1 + int(mp.rand() * 10)
n = 1 + int(mp.rand() * 10)
A = (2 * mp.randmatrix(m, n) - 1) + 1j * (2 * mp.randmatrix(m, n) - 1)
if mp.rand() > 0.5:
A *= 10
for x in xrange(m):
for y in xrange(n):
A[x,y]=int(mp.re(A[x,y])) + 1j * int(mp.im(A[x,y]))
run_svd_c(A, full_matrices=full, verbose=False)
def test_svd_r_rand():
for i in xrange(5):
full = mp.rand() > 0.5
m = 1 + int(mp.rand() * 10)
n = 1 + int(mp.rand() * 10)
A = 2 * mp.randmatrix(m, n) - 1
if mp.rand() > 0.5:
A *= 10
for x in xrange(m):
for y in xrange(n):
A[x, y] = int(A[x, y])
run_svd_r(A, full_matrices=full, verbose=False)
def test_svd_r_rand():
for i in xrange(5):
full = mp.rand() > 0.5
m = 1 + int(mp.rand() * 10)
n = 1 + int(mp.rand() * 10)
A = 2 * mp.randmatrix(m, n) - 1
if mp.rand() > 0.5:
A *= 10
for x in xrange(m):
for y in xrange(n):
A[x,y]=int(A[x,y])
run_svd_r(A, full_matrices = full, verbose = False)
def irandmatrix(n, range=10):
"""
random matrix with integer entries
"""
A = mp.matrix(n, n)
for i in xrange(n):
for j in xrange(n):
A[i, j] = int((2 * mp.rand() - 1) * range)
return A
def irandmatrix(n, range = 10):
"""
random matrix with integer entries
"""
A = mp.matrix(n, n)
for i in xrange(n):
for j in xrange(n):
A[i,j]=int( (2 * mp.rand() - 1) * range)
return A
def test_eig_dyn():
v = 0
for i in xrange(5):
n = 1 + int(mp.rand() * 5)
if mp.rand() > 0.5:
# real
A = 2 * mp.randmatrix(n, n) - 1
if mp.rand() > 0.5:
A *= 10
for x in xrange(n):
for y in xrange(n):
A[x,y] = int(A[x,y])
else:
A = (2 * mp.randmatrix(n, n) - 1) + 1j * (2 * mp.randmatrix(n, n) - 1)
if mp.rand() > 0.5:
A *= 10
for x in xrange(n):
for y in xrange(n):
A[x,y] = int(mp.re(A[x,y])) + 1j * int(mp.im(A[x,y]))
run_hessenberg(A, verbose = v)
run_schur(A, verbose = v)
run_eig(A, verbose = v)
v0 = 0
v1 = 2
duration = mp.mpf('5')
vm = 2.5
am = 1.8
prevCurve = Interpolate1D(x0, x1, v0, v1, vm, am)
fixedCurve = interpolation._Stretch1D(prevCurve, duration, vm, am)
# Check if the interpolation preserves the displacement
assert (ramp.IsEqual(prevCurve.d, 3))
assert (ramp.IsEqual(fixedCurve.d, 3))
# Check if the interpolation preserves the duration
assert (ramp.IsEqual(fixedCurve.duration, duration))
x0 = 0
x1 = mp.rand() * 3
v0 = 0
v1 = mp.rand() * 2
duration = mp.mpf('5')
vm = 2.5
am = 1.8
prevCurve = Interpolate1D(x0, x1, v0, v1, vm, am)
fixedCurve = interpolation._Stretch1D(prevCurve, duration, vm, am)
# Check if the interpolation preserves the displacement
assert (ramp.IsEqual(prevCurve.d, mp.mpf(str(x1))))
assert (ramp.IsEqual(fixedCurve.d, mp.mpf(str(x1))))
# Check if the interpolation preserves the duration
assert (ramp.IsEqual(fixedCurve.duration, duration))
nTrials = 0
import numpy as np
import time as tmf
import matplotlib.pyplot as plt
#------------------------------------------------------------------------------
# mpmath: free (BSD licensed) Python library for real and complex floating-point
# arithmetic with arbitrary precision. http://mpmath.org/
from mpmath import mp
#------------------------------------------------------------------------------
dps = np.arange(10, 2000, 10)
T = np.zeros(len(dps))
samples = 1000
for si in range(samples):
for i in range(len(dps)):
mp.dps = dps[i]
number1, number2 = mp.rand(), mp.rand()
t0 = tmf.clock()
C = mp.sqrt(number1) * mp.sqrt(number2)
T[i] += (tmf.clock() - t0) / samples
plt.plot(dps, T)
v0 = 0
v1 = 2
duration = mp.mpf('5')
vm = 2.5
am = 1.8
prevCurve = Interpolate1D(x0, x1, v0, v1, vm, am)
fixedCurve = interpolation._Stretch1D(prevCurve, duration, vm, am)
# Check if the interpolation preserves the displacement
assert(ramp.IsEqual(prevCurve.d, 3))
assert(ramp.IsEqual(fixedCurve.d, 3))
# Check if the interpolation preserves the duration
assert(ramp.IsEqual(fixedCurve.duration, duration))
x0 = 0
x1 = mp.rand()*3
v0 = 0
v1 = mp.rand()*2
duration = mp.mpf('5')
vm = 2.5
am = 1.8
prevCurve = Interpolate1D(x0, x1, v0, v1, vm, am)
fixedCurve = interpolation._Stretch1D(prevCurve, duration, vm, am)
# Check if the interpolation preserves the displacement
assert(ramp.IsEqual(prevCurve.d, mp.mpf(str(x1))))
assert(ramp.IsEqual(fixedCurve.d, mp.mpf(str(x1))))
# Check if the interpolation preserves the duration
assert(ramp.IsEqual(fixedCurve.duration, duration))
nTrials = 0
