In this commit, we fix an existing bug that could at times lead to a
panic if a user manually crafts a route via SendToRoute, and that route
results in a payment error. The fix is simple: create the map even
though it won't be used in the sessions since the user is feeding the
router manual routes.
In this commit, we fix an issue where we would always assume the dust
limit was Bitcoin's dust limit, rather than the active chain. This would
lead to issues when attempting to open channels on the Litecon chain.
In this commit, we alter the behavior of the regular
short channel id encoding, such that it returns a nil
slice if the decoded number of elements is 0. This is
done so that it matches the behavior of the zlib
decompression, allowing us to test both in using the
same corpus.
Modifies the behavior of the quick test for
MsgQueryShortChanIDs, such that the generated
slice of expected short chan ids is always nil
if no elements are returned. This mimics the
behavior of the zlib decompression, where
elements are appended to the slice, instead of
assigning to preallocated slice.
Makes the helper methods for constructing witness script
hash and to-local outputs. This will allow watchtowers to
import and reuse this logic when sweeping outputs.
In this commit, we update the generateSphinxPacket to use newLogClosure
to delay the spew evaluation until log print time. Before this commit,
even if we weren't on the trace logging level, the spew call would
always be evaluated.
In this commit, we fix a slight regression that happened when the
sendtoroute RPC call was implemented. As a result of the refactoring,
the debughtlc mode silently broke. In this commit, we fix this by
checking against the zero hash, rather than the length of the payment
hash, as since we use a fixed sized byte slice, it will always be zero.
This commit corrects the distribution used to
schedule a link's randomized backoff for fee
updates. Currently, our algorithm biases the
lowest value in the range, with probability
equal to lower/upper, or the ratio of the lower
bound to the upper. This distribution is skewed
more heavily as lower approaches upper.
The solution is to sample a random value in the
range upper-lower, then add this to our lower
bound. The effect is a uniformly distributed
timeout in [lower, upper).
