Cluster Range

class blocksci.cluster.ClusterRange

address_count() → numpy.ndarray[int]

For each item: The number of addresses in the cluster

property addresses

For each item: Get a iterable over all the addresses in the cluster

Type

blocksci.AddressIterator

balance(height: int = - 1) → numpy.ndarray[int]

For each item: Calculates the balance held by this cluster at the height (Defaults to the full chain)

count_of_type(address_type: blocksci::AddressType::Enum) → numpy.ndarray[int]

For each item: Return the number of addresses of the given type in the cluster

in_txes_count() → numpy.ndarray[int]

For each item: Return the number of transactions where this cluster was an input

property index

For each item: The internal identifier of the cluster

Type

numpy.ndarray[int]

input_txes_count() → numpy.ndarray[int]

For each item: Return the number of transactions where this cluster was an input

inputs() → blocksci.InputIterator

For each item: Returns an iterator over all inputs spent from this cluster

ins() → blocksci.InputIterator

For each item: Returns an iterator over all inputs spent from this cluster

out_txes_count() → numpy.ndarray[int]

For each item: Return the number of transactions where an address in this cluster was used in an output

output_txes_count() → numpy.ndarray[int]

For each item: Return the number of transactions where an address in this cluster was used in an output

outputs() → blocksci.OutputIterator

For each item: Returns an iterator over all outputs sent to this cluster

outs() → blocksci.OutputIterator

For each item: Returns an iterator over all outputs sent to this cluster

tagged_addresses(tagged_addresses: Dict[Address, str]) → blocksci.cluster.TaggedAddressIterator

For each item: Given a dictionary of tags, return a range of TaggedAddress objects for any tagged addresses in the cluster

property type_equiv_size

For each item: The number of addresses in the cluster not counting type equivalent addresses

Type

numpy.ndarray[int]