suggested points lie outside of domain about adaptive

[basnijholt]

originally posted by Bas Nijholt (@basnijholt) at 2018-12-14T13:42:02.648Z on GitLab

adding assert self.inside_bounds(point_new) inside _fill_stack to see what the problem is with np.clip:

> /Users/basnijholt/Sync/Work/adaptive/adaptive/learner/learner2D.py(439)_fill_stack()
    437             point_new = tuple(self._unscale(point_new))
    438 
--> 439             assert self.inside_bounds(point_new)
    440             # np.clip results in numerical precision problems
    441             # https://gitlab.kwant-project.org/qt/adaptive/issues/132

ipdb> point_new
(0.18, 0.5999999999999999)
ipdb> np.clip(point_new, *self.bounds)
array([0.18, 0.6 ])
ipdb> np.clip(point_new, *self.bounds) == point_new
array([ True,  True])
ipdb> self.inside_bounds(np.clip(point_new, *self.bounds))
False
ipdb> clip = lambda x, l, u: max(l, min(u, x))
ipdb> self.inside_bounds((clip(point_new[0], *self.bounds[0]), clip(point_new[1], *self.bounds[1])))
True

[basnijholt]

originally posted by Bas Nijholt (@basnijholt) at 2018-12-14T14:10:58.534Z on GitLab

To get the numbers:

import numpy as np
import pickle
tup = pickle.loads(b'\x80\x03cnumpy.core.multiarray\nscalar\nq\x00cnumpy\ndtype\nq\x01X\x02\x00\x00\x00f8q\x02K\x00K\x01\x87q\x03Rq\x04(K\x03X\x01\x00\x00\x00<q\x05NNNJ\xff\xff\xff\xffJ\xff\xff\xff\xffK\x00tq\x06bC\x08\n\xd7\xa3p=\n\xc7?q\x07\x86q\x08Rq\th\x00h\x04C\x08233333\xe3?q\n\x86q\x0bRq\x0c\x86q\r.')
bounds = ((0.16, 0.2), (0.6, 2.4))

def inside_bounds(xy):
    x, y = xy
    (xmin, xmax), (ymin, ymax) = bounds
    return xmin <= x <= xmax and ymin <= y <= ymax

def my_clip(xy):
    clip = lambda x, l, u: max(l, min(u, x))
    return (clip(xy[0], *bounds[0]),
            clip(xy[1], *bounds[1]))

(inside_bounds(tup),
 inside_bounds(np.array(tup)),
 inside_bounds(np.clip(tup, *bounds)),
 inside_bounds(my_clip(tup)),
)

(False, False, False, True)