  Given a random sample from some unknown density $f_0: \mathbb R \to [0,
\infty)$ we devise Haar wavelet estimators for $f_0$ with variable resolution
levels constructed from localised test procedures (as in Lepski, Mammen, and
Spokoiny (1997, Ann. Statist.)). We show that these estimators adapt to
spatially heterogeneous smoothness of $f_0$, simultaneously for every point $x$
in a fixed interval, in sup-norm loss. The thresholding constants involved in
the test procedures can be chosen in practice under the idealised assumption
that the true density is locally constant in a neighborhood of the point $x$ of
estimation, and an information theoretic justification of this practice is
given.
