Cluster Range¶

class blocksci.ClusterRange¶

addresses¶

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

Type:	`blocksci.AddressIterator`

balance(height: int=-1) → numpy.ndarray[int64]¶

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

Return type:	`numpy.ndarray[int64]`

count_of_type(address_type: blocksci.address_type) → numpy.ndarray[uint32]¶

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

Return type:	`numpy.ndarray[uint32]`

in_txes_count() → numpy.ndarray[int64]¶

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

Return type:	`numpy.ndarray[int64]`

index¶

For each item: The internal identifier of the cluster

Type:	`numpy.ndarray[uint32]`

out_txes_count() → numpy.ndarray[int64]¶

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

Return type:	`numpy.ndarray[int64]`

outs() → blocksci.OutputIterator¶

For each item: Returns a list of all outputs sent to this cluster

Return type:	`blocksci.OutputIterator`

size() → numpy.ndarray[int64]¶

For each item: The number of addresses in the cluster

Return type:	`numpy.ndarray[int64]`

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

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

Return type:	`blocksci.TaggedAddressIterator`

type_equiv_size¶

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

Type:	`numpy.ndarray[int64]`