'extrap': False,
'arrays': False,
}
#surface.doit(bounds, stop, step=step)
#'multiquadric','inverse','gaussian','linear','cubic','quintic','thin_plate'
# impose data filter (so X >= 0 and Y >= 0)
from mystic.filters import generate_mask, generate_filter
from mystic.constraints import impose_bounds
_bounds = impose_bounds((0.0, None))(lambda x:x)
filter = generate_filter(generate_mask(_bounds, _bounds))
# from numpy import round as _round
#############
# get trajectories
surface.Sample(bounds, stop, all=all, filter=filter)#, constraints=_round)
print("TOOK: %s" % (time.time() - start))
# exit()
# get interpolated function
surface.Interpolate(**args)
# check extrema #XXX: put _min,_max in Interpolate? (downsampled)
f = lambda x,z: (z,surface.surrogate(*x))
print("min: {}; min@f: {}".format(*f(*surface._min())))
print("max: {}; max@f: {}".format(*f(*surface._max())))
# print("TOOK: %s" % (time.time() - start))
# plot interpolated surface
axes = (0,1)
vals = () # use remaining minima as the fixed values
surface.Plot(step=step, scale=scale, shift=shift, density=density, axes=axes, vals=vals)
density = 9
shift = 0
scale = 0
step = 200
args = {
#'smooth': 0,
'method': 'thin_plate',
'extrap': False,
'arrays': False,
}
#surface.doit(bounds, stop, step=step)
#'multiquadric','inverse','gaussian','linear','cubic','quintic','thin_plate'
#############
# get trajectories
surface.Sample(bounds, stop, all=all)
print("TOOK: %s" % (time.time() - start))
# exit()
# get interpolated function
surface.Interpolate(**args)
# check extrema #XXX: put _min,_max in Interpolate? (downsampled)
f = lambda x, z: (z, surface.surrogate(*x))
print("min: {}; min@f: {}".format(*f(*surface._min())))
print("max: {}; max@f: {}".format(*f(*surface._max())))
# print("TOOK: %s" % (time.time() - start))
# plot surface
axes = (0, 1)
vals = () # use remaining minima as the fixed values
surface.Plot(step=step,
