def run_grid_interpolation(tfunc, dct, N, low, high, scale=1.e-8):
Abscissas = Ostap.Math.Interpolation.Abscissas
## uniform abscissas
i0 = interpolate(dct)
## bernstein interpolant
i1 = interpolate_bernstein(dct, None, low, high)
xx = []
for i in range(100000):
xx.append(random.uniform(low, high))
xx.sort()
c0 = SE()
c1 = SE()
for x in xx:
f = tfunc(x)
f0 = i0(x)
f1 = i1(x)
d0 = f0 - f
d1 = f1 - f
c0 += abs(d0) / scale
c1 += abs(d1) / scale
logger.info('Grid precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' %
(scale, c0.mean().value(), c0.rms(), c0.max()))
logger.info('Bernstein precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' %
(scale, c1.mean().value(), c1.rms(), c1.max()))
def run_func_interpolation ( fun , N , low , high , scale = 1.e-8 ) :
Abscissas = Ostap.Math.Interpolation.Abscissas
## uniform abscissas
i0 = interpolate ( fun , Abscissas ( N , low , high , 0 ) )
## Chebyshev abscissas
i1 = interpolate ( fun , Abscissas ( N , low , high , 1 ) )
## lobatto abscissas
i2 = interpolate ( fun , Abscissas ( N , low , high , 2 ) )
## random abscissas
i3 = interpolate ( fun , [ random.uniform ( low , high ) for i in range (N ) ] )
## bernstein interpolant
i4 = interpolate_bernstein ( fun , Abscissas ( N , low , high , 2 ) , low , high )
xx = []
for i in range ( 100000 ) : xx.append ( random.uniform ( low , high ) )
xx.sort()
c0 = SE()
c1 = SE()
c2 = SE()
c3 = SE()
c4 = SE()
for x in xx :
f = fun ( x )
f0 = i0 ( x )
f1 = i1 ( x )
f2 = i2 ( x )
f3 = i3 ( x )
f4 = i4 ( x )
d0 = f0 - f
d1 = f1 - f
d2 = f2 - f
d3 = f3 - f
d4 = f4 - f
c0 += abs ( d0 ) / scale
c1 += abs ( d1 ) / scale
c2 += abs ( d2 ) / scale
c3 += abs ( d3 ) / scale
c4 += abs ( d4 ) / scale
logger.info ( 'Uniform precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c0.mean () . value () , c0.rms () , c0.max () ) )
logger.info ( 'Chebyshev precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c1.mean () . value () , c1.rms () , c1.max () ) )
logger.info ( 'Lobatto precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c2.mean () . value () , c2.rms () , c2.max () ) )
logger.info ( 'Random precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c3.mean () . value () , c3.rms () , c3.max () ) )
logger.info ( 'Bernstein precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c4.mean () . value () , c4.rms () , c4.max () ) )
def run_func_interpolation(fun,
N,
low,
high,
scale=1.e-5,
logger=logger,
name='Interpolation'):
"""Interpolate the function"""
Abscissas = Ostap.Math.Interpolation.Abscissas
abscissas = (('Uniform', Abscissas(N, low, high, 0)),
('Chebyshev', Abscissas(N, low, high,
1)), ('Lobatto',
Abscissas(N, low, high, 2)),
('Random',
Abscissas(doubles([x for x in more_uniform(low, high, N, N)
]))))
tables = [(a[0], points(fun, a[1])) for a in abscissas]
interpolants = []
for i, t in enumerate(tables):
item = ('Bernstein',
t[0]), interpolate_bernstein(t[1], None, low, high)
interpolants.append(item)
item = ('Neville', t[0]), Ostap.Math.Neville(t[1])
interpolants.append(item)
item = ('Lagrange', t[0]), Ostap.Math.Lagrange(t[1])
interpolants.append(item)
item = ('Newton', t[0]), Ostap.Math.Newton(t[1])
interpolants.append(item)
item = ('Berrut1st', t[0]), Ostap.Math.Berrut1st(t[1])
interpolants.append(item)
item = ('Berrut2nd', t[0]), Ostap.Math.Berrut2nd(t[1])
interpolants.append(item)
item = ('Barycentric', t[0]), Ostap.Math.Barycentric(t[1])
interpolants.append(item)
item = ('Thiele', t[0]), Ostap.Math.Thiele(t[1])
interpolants.append(item)
for d in range(0, 9):
item = ('FloaterHormann%d' % d,
t[0]), Ostap.Math.FloaterHormann(t[1], d)
interpolants.append(item)
for d in range(1, 6):
item = ('BSpline%d' % d, t[0]), interpolate_bspline(t[1], None, d)
interpolants.append(item)
if bspline_interpolate:
for d in (1, 3, 5, 7):
item = ('BSpline%dSP' % d, t[0]), bspline_interpolate(t[1], d)
interpolants.append(item)
for n, t in interpolants:
functions.add((n, t))
graphs = []
with wait(3), use_canvas(name):
ff = lambda x: fun(x)
f1_draw(ff, xmin=low, xmax=high, linecolor=2, linewidth=2)
for i, item in enumerate(interpolants):
p, f = item
n1, n2 = p
color = i + 3
f.draw('same', linecolor=color, xmin=low, xmax=high)
if hasattr(f, 'graph'):
g = f.graph()
g.draw('p', markercolor=color, markersize=2)
graphs.append(g)
if 1 == color: color = 'Black'
elif 2 == color: color = 'Red'
elif 3 == color: color = 'Green'
elif 4 == color: color = 'Blue'
elif 5 == color: color = 'Yellow'
elif 6 == color: color = 'Magenta'
elif 7 == color: color = 'Cyan'
elif 8 == color: color = 'DarkGreen'
logger.info('Color %10s for %s:%s' % (color, n1, n2))
graphs = [t[1].graph() for t in tables]
for i, g in enumerate(graphs, start=3):
g.draw('p', markercolor=i)
xx = []
NP = 50000
for i in range(NP):
xx.append(random.uniform(low, high))
xx.sort()
from collections import defaultdict
counters = defaultdict(SE)
cpu = {}
## loop over all interpolants
for n, fi in interpolants:
n1, n2 = n
cnt = counters[(n1, n2, fi)]
with timing('', logger=None) as t:
for x in xx:
v = fun(x)
vi = fi(x)
cnt += abs(vi - v) / scale
cpu[(n1, n2)] = t.delta
rows = [('Interpolant', 'Grid', 'mean+/-rms', 'max', 'distance')]
for item in counters:
n1, n2, ff = item
c = counters[item]
d = distance(ff, fun, low, high) / scale
vmax = c.max()
if 1.e+6 < vmax: vmax = '+inf'
else: vmax = '%-9.1f' % vmax
row = n1, n2, '%9.2f +/- %-09.1f' % (c.mean().value(),
c.rms()), vmax, '%.3g' % d
rows.append(row)
title = 'Interpolation precision (%d random points)[x%s]' % (NP, scale)
table = T.table(rows, title=title, prefix='# ', alignment='lllll')
logger.info('%s:\n%s' % (title, table))
lst = []
for k in cpu:
item = cpu[k], k[0], k[1]
lst.append(item)
lst.sort()
rows = [('Interpolant', 'Grid', 'CPU [s]')]
for t, k0, k1 in lst:
row = k0, k1, '%.4g' % cpu[(k0, k1)]
rows.append(row)
title = 'CPU: %d points' % NP
table = T.table(rows, title=title, prefix='# ', alignment='ll')
logger.info('%s:\n%s' % (title, table))
def run_grid_interpolation ( tfunc , dct , N , low , high , scale = 1.e-8 ) :
Abscissas = Ostap.Math.Interpolation.Abscissas
data = points ( dct )
## uniform abscissas
i0 = interpolate ( data )
## bernstein interpolant
i1 = interpolate_bernstein ( data , None , low , high )
## neville interpolant
i2 = Ostap.Math.Neville ( data )
## largange interpolant
i3 = Ostap.Math.Lagrange ( data )
## newton interpolant
i4 = Ostap.Math.Newton ( data )
## bspline interpolant
degree = 3
## bs = Ostap.Math.BSpline ( low , high , len ( data ) - 1 - degree , degree )
i5 = interpolate_bspline ( data , None , degree )
xx = []
for i in range ( 100000 ) : xx.append ( random.uniform ( low , high ) )
xx.sort()
c0 = SE ()
c1 = SE ()
c2 = SE ()
c3 = SE ()
c4 = SE ()
c5 = SE ()
for x in xx :
f = tfunc ( x )
f0 = i0 ( x )
f1 = i1 ( x )
f2 = i2 ( x )
f3 = i3 ( x )
f4 = i4 ( x )
f5 = i5 ( x )
d0 = f0 - f
d1 = f1 - f
d2 = f2 - f
d3 = f3 - f
d4 = f4 - f
d5 = f5 - f
c0 += abs ( d0 ) / scale
c1 += abs ( d1 ) / scale
c2 += abs ( d2 ) / scale
c3 += abs ( d3 ) / scale
c4 += abs ( d4 ) / scale
c5 += abs ( d5 ) / scale
logger.info ( 'Grid precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c0.mean () . value () , c0.rms () , c0.max () ) )
logger.info ( 'Bernstein precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c1.mean () . value () , c1.rms () , c1.max () ) )
logger.info ( 'Neville precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c2.mean () . value () , c2.rms () , c2.max () ) )
logger.info ( 'Lagrange precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c3.mean () . value () , c3.rms () , c3.max () ) )
logger.info ( 'Newton precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c4.mean () . value () , c4.rms () , c4.max () ) )
logger.info ( 'bSpline precision: mean/max[%s] = %9.2f +- %-09.1f/%-9.1f' % ( scale , c5.mean () . value () , c5.rms () , c5.max () ) )
import time
i5.draw()
time.sleep(5)
def run_grid_interpolation(tfunc,
dct,
N,
low,
high,
scale=1.e-8,
logger=logger,
name='interpolation'):
"""Interpolate the grid"""
Abscissas = Ostap.Math.Interpolation.Abscissas
data = points(dct)
## list of interpolants
interpolants = []
## bernstein interpolant
interpolants.append(
('Bernstein', interpolate_bernstein(data, None, low, high)))
## neville interpolant
interpolants.append(('Neville', Ostap.Math.Neville(data)))
## largange interpolant
interpolants.append(('Lagrange', Ostap.Math.Lagrange(data)))
## (true) Barycentric interpolant
interpolants.append(('Barycentric', Ostap.Math.Barycentric(data)))
## Newton interpolant
interpolants.append(('Newton', Ostap.Math.Newton(data)))
## 1st Berrut interpolant
interpolants.append(('Berrut 1st', Ostap.Math.Berrut1st(data)))
## 2nd Berrut interpolant
interpolants.append(('Berrut 2nd', Ostap.Math.Berrut2nd(data)))
## Thiele rational interpolant
interpolants.append(('Thiele', Ostap.Math.Thiele(data)))
for d in range(10):
interpolants.append(
('FloaterHormann/%d' % d, Ostap.Math.FloaterHormann(data, d)))
## bspline interpolant (Ostap)
for d in range(1, 6):
interpolants.append(
('BSpline/%s' % d, interpolate_bspline(data, None, d)))
## bspline interpolant (scipy)
if bspline_interpolate:
for d in range(1, 9, 2):
item = ('BSpline%dSP' % d, bspline_interpolate(data, d))
interpolants.append(item)
for n, t in interpolants:
functions.add((n, t))
graphs = []
with wait(1), use_canvas(name):
ff = lambda x: tfunc(x)
f1_draw(ff, xmin=low, xmax=high, linecolor=1, linewidth=3)
for i, c in enumerate(interpolants):
n, f = c
color = i + 2
f.draw('same', linecolor=color, xmin=low, xmax=high)
if hasattr(f, 'graph'):
g = f.graph()
g.draw('p', markercolor=color, markersize=2)
graphs.append(g)
if 1 == color: color = 'Black'
elif 2 == color: color = 'Red'
elif 3 == color: color = 'Green'
elif 4 == color: color = 'Blue'
elif 5 == color: color = 'Yellow'
elif 6 == color: color = 'Magenta'
elif 7 == color: color = 'Cyan'
elif 8 == color: color = 'DarkGreen'
logger.info('Color %10s for %s' % (color, n))
xx = []
NP = 50000
for i in range(NP):
xx.append(random.uniform(low, high))
xx.sort()
from collections import defaultdict
counters = defaultdict(SE)
cpu = {}
## loop over all interpolants
for n, fi in interpolants:
cnt = counters[(n, fi)]
with timing('', logger=None) as t:
for x in xx:
v = tfunc(x)
vi = fi(x)
cnt += abs(vi - v) / scale
cpu[n] = t.delta
rows = [('Configuration', 'mean+/-rms', 'max', 'distance')]
for item in counters:
n, ff = item
c = counters[item]
d = distance(ff, tfunc, low, high) / scale
row = n, '%9.2f +/- %-09.1f' % (
c.mean().value(), c.rms()), '%-9.1f' % c.max(), '%.3g' % d
rows.append(row)
title = 'Interpolation precision (%d random points)[x%s]' % (NP, scale)
table = T.table(rows, title=title, prefix='# ', alignment='llll')
logger.info('%s:\n%s' % (title, table))
lst = []
for k in cpu:
item = cpu[k], k
lst.append(item)
lst.sort()
rows = [('Interpolant', 'CPU [s]')]
for t, k in lst:
row = k, '%.4g' % cpu[k]
rows.append(row)
title = 'CPU: %d points' % NP
table = T.table(rows, title=title, prefix='# ', alignment='ll')
logger.info('%s:\n%s' % (title, table))