/// Create a pruned Merkle Mountain Range from the provided MMR. Pruning entails throwing all the hashes of the
/// pruned MMR away, except for the current peaks. A new MMR instance is returned that allows you to continue
/// adding onto the MMR as before. Most functions of the pruned MMR will work as expected, but obviously, any
/// leaf hashes prior to the base point won't be available. `get_leaf_hash` will return `None` for those nodes, and
/// `validate` will throw an error.
Create a pruned Merkle Mountain Range from the provided MMR. Pruning entails throwing all the hashes of the
pruned MMR away, except for the current peaks. A new MMR instance is returned that allows you to continue
adding onto the MMR as before. Most functions of the pruned MMR will work as expected, but obviously, any
leaf hashes prior to the base point won't be available.
Create a pruned Merkle Mountain Range from the provided MMR . Pruning entails throwing all the hashes of the pruned MMR away except for the current peaks . A new MMR instance is returned that allows you to continue adding onto the MMR as before . Most functions of the pruned MMR will work as expected but obviously any leaf hashes prior to the base point won ' t be available .
pub fn prune_mmr<D, B>(mmr: &MerkleMountainRange<D, B>) -> Result<PrunedMmr<D>, MerkleMountainRangeError>
where
    D: Digest,
    B: ArrayLike<Value = Hash>,
{
    let backend = PrunedHashSet::try_from(mmr)?;
    Ok(MerkleMountainRange {
        hashes: backend,
        _hasher: PhantomData,
    })
}
pub fn prune_mmr < D , B > ( mmr : & MerkleMountainRange < D , B > ) -> Result < PrunedMmr < D > , MerkleMountainRangeError > where D : Digest , B : ArrayLike < Value = Hash > , { let backend = PrunedHashSet :: try_from ( mmr ) ? ; Ok ( MerkleMountainRange { hashes : backend , _hasher : PhantomData , } ) }
Create a pruned Merkle Mountain Range from the provided MMR.
Create a pruned Merkle Mountain Range from the provided MMR .
mmr
&MerkleMountainRange<D, B>