#!/usr/bin/env python
"""
An implementation of sequence bloom trees, Solomon & Kingsford, 2015.
To try it out, do::
factory = GraphFactory(ksize, tablesizes, n_tables)
root = Node(factory)
graph1 = factory()
# ... add stuff to graph1 ...
leaf1 = Leaf("a", graph1)
root.insert(leaf1)
For example, ::
# filenames: list of fa/fq files
# ksize: k-mer size
# tablesizes: Bloom filter table sizes
# n_tables: Number of tables
factory = GraphFactory(ksize, tablesizes, n_tables)
root = Node(factory)
for filename in filenames:
graph = factory()
graph.consume_fasta(filename)
leaf = Leaf(filename, graph)
root.insert(leaf)
then define a search function, ::
def kmers(k, seq):
for start in range(len(seq) - k + 1):
yield seq[start:start + k]
def search_transcript(node, seq, threshold):
presence = [ node.data.get(kmer) for kmer in kmers(ksize, seq) ]
if sum(presence) >= int(threshold * len(seq)):
return 1
return 0
"""
from __future__ import print_function, unicode_literals, division
from collections import namedtuple
try:
from collections.abc import Mapping
except ImportError: # Python 2...
from collections import Mapping
from copy import copy
import json
import math
import os
from random import randint, random
import sys
from tempfile import NamedTemporaryFile
from deprecation import deprecated
from .exceptions import IndexNotSupported
from .sbt_storage import FSStorage, TarStorage, IPFSStorage, RedisStorage, ZipStorage
from .logging import error, notify, debug
from .index import Index
from .nodegraph import Nodegraph, extract_nodegraph_info, calc_expected_collisions
STORAGES = {
'TarStorage': TarStorage,
'FSStorage': FSStorage,
'IPFSStorage': IPFSStorage,
'RedisStorage': RedisStorage,
'ZipStorage': ZipStorage,
}
NodePos = namedtuple("NodePos", ["pos", "node"])
[docs]class GraphFactory(object):
"""Build new nodegraphs (Bloom filters) of a specific (fixed) size.
Parameters
----------
ksize: int
k-mer size.
starting_size: int
size (in bytes) for each nodegraph table.
n_tables: int
number of nodegraph tables to be used.
"""
def __init__(self, ksize, starting_size, n_tables):
self.ksize = ksize
self.starting_size = starting_size
self.n_tables = n_tables
def __call__(self):
return Nodegraph(self.ksize, self.starting_size, self.n_tables)
[docs] def init_args(self):
return (self.ksize, self.starting_size, self.n_tables)
[docs]class SBT(Index):
"""A Sequence Bloom Tree implementation allowing generic internal nodes and leaves.
The default node and leaf format is a Bloom Filter (like the original implementation),
but we also provide a MinHash leaf class (in the sourmash.sbtmh.SigLeaf class)
Parameters
----------
factory: Factory
Callable for generating new datastores for internal nodes.
d: int
Number of children for each internal node. Defaults to 2 (a binary tree)
storage: Storage, default: None
A Storage is any place where we can save and load data for the nodes.
If set to None, will use a FSStorage.
Notes
-----
We use two dicts to store the tree structure: One for the internal nodes,
and another for the leaves (datasets).
"""
def __init__(self, factory, d=2, storage=None):
self.factory = factory
self._nodes = {}
self._missing_nodes = set()
self._leaves = {}
self.d = d
self.next_node = 0
self.storage = storage
[docs] def signatures(self):
for k in self.leaves():
yield k.data
[docs] def select(self, ksize=None, moltype=None):
first_sig = next(iter(self.signatures()))
ok = True
if ksize is not None and first_sig.minhash.ksize != ksize:
ok = False
if moltype is not None and first_sig.moltype != moltype:
ok = False
if ok:
return self
raise ValueError("cannot select SBT on ksize {} / moltype {}".format(ksize, moltype))
[docs] def new_node_pos(self, node):
if not self._nodes:
self.next_node = 1
return 0
if not self._leaves:
self.next_node = 2
return 1
min_leaf = min(self._leaves.keys())
next_internal_node = None
if self.next_node <= min_leaf:
for i in range(min_leaf):
if all((i not in self._nodes,
i not in self._leaves,
i not in self._missing_nodes)):
next_internal_node = i
break
if next_internal_node is None:
self.next_node = max(self._leaves.keys()) + 1
else:
self.next_node = next_internal_node
return self.next_node
[docs] def insert(self, signature):
"Add a new SourmashSignature in to the SBT."
from .sbtmh import SigLeaf
leaf = SigLeaf(signature.md5sum(), signature)
self.add_node(leaf)
[docs] def add_node(self, node):
pos = self.new_node_pos(node)
if pos == 0: # empty tree; initialize w/node.
n = Node(self.factory, name="internal." + str(pos))
self._nodes[0] = n
pos = self.new_node_pos(node)
# Cases:
# 1) parent is a Leaf (already covered)
# 2) parent is a Node (with empty position available)
# - add Leaf, update parent
# 3) parent is a Node (no position available)
# - this is covered by case 1
# 4) parent is None
# this can happen with d != 2, in this case create the parent node
p = self.parent(pos)
if isinstance(p.node, Leaf):
# Create a new internal node
# node and parent are children of new internal node
n = Node(self.factory, name="internal." + str(p.pos))
self._nodes[p.pos] = n
c1, c2 = self.children(p.pos)[:2]
self._leaves[c1.pos] = p.node
self._leaves[c2.pos] = node
del self._leaves[p.pos]
for child in (p.node, node):
child.update(n)
elif isinstance(p.node, Node):
self._leaves[pos] = node
node.update(p.node)
elif p.node is None:
n = Node(self.factory, name="internal." + str(p.pos))
self._nodes[p.pos] = n
c1 = self.children(p.pos)[0]
self._leaves[c1.pos] = node
node.update(n)
# update all parents!
p = self.parent(p.pos)
while p:
self._rebuild_node(p.pos)
node.update(self._nodes[p.pos])
p = self.parent(p.pos)
[docs] def find(self, search_fn, *args, **kwargs):
"Search the tree using `search_fn`."
unload_data = kwargs.get("unload_data", False)
# initialize search queue with top node of tree
matches = []
visited, queue = set(), [0]
# while the queue is not empty, load each node and apply search
# function.
while queue:
node_p = queue.pop(0)
# repair while searching.
node_g = self._leaves.get(node_p, None)
if node_g is None:
node_g = self._nodes.get(node_p, None)
if node_g is None:
if node_p in self._missing_nodes:
self._rebuild_node(node_p)
node_g = self._nodes[node_p]
else:
continue
# if we have not visited this node before,
if node_p not in visited:
visited.add(node_p)
# apply search fn. If return false, truncate search.
if search_fn(node_g, *args):
# leaf node? it's a match!
if isinstance(node_g, Leaf):
matches.append(node_g)
# internal node? descend.
elif isinstance(node_g, Node):
if kwargs.get('dfs', True): # defaults search to dfs
for c in self.children(node_p):
queue.insert(0, c.pos)
else: # bfs
queue.extend(c.pos for c in self.children(node_p))
if unload_data:
node_g.unload()
return matches
[docs] def search(self, query, *args, **kwargs):
from .sbtmh import search_minhashes, search_minhashes_containment
from .sbtmh import SearchMinHashesFindBest
from .signature import SourmashSignature
threshold = kwargs['threshold']
ignore_abundance = kwargs['ignore_abundance']
do_containment = kwargs['do_containment']
best_only = kwargs['best_only']
unload_data = kwargs.get('unload_data', False)
# figure out scaled value of tree, downsample query if needed.
leaf = next(iter(self.leaves()))
tree_mh = leaf.data.minhash
tree_query = query
if tree_mh.scaled and query.minhash.scaled and \
tree_mh.scaled > query.minhash.scaled:
resampled_query_mh = tree_query.minhash
resampled_query_mh = resampled_query_mh.downsample_scaled(tree_mh.scaled)
tree_query = SourmashSignature(resampled_query_mh)
# define both search function and post-search calculation function
search_fn = search_minhashes
query_match = lambda x: tree_query.similarity(
x, downsample=False, ignore_abundance=ignore_abundance)
if do_containment:
search_fn = search_minhashes_containment
query_match = lambda x: tree_query.contained_by(x, downsample=True)
if best_only: # this needs to be reset for each SBT
search_fn = SearchMinHashesFindBest().search
# now, search!
results = []
for leaf in self.find(search_fn, tree_query, threshold, unload_data=unload_data):
similarity = query_match(leaf.data)
# tree search should always/only return matches above threshold
assert similarity >= threshold
results.append((similarity, leaf.data, None))
return results
[docs] def gather(self, query, *args, **kwargs):
"Return the match with the best Jaccard containment in the database."
from .sbtmh import GatherMinHashes
if not query.minhash: # empty query? quit.
return []
# use a tree search function that keeps track of its best match.
search_fn = GatherMinHashes().search
unload_data = kwargs.get('unload_data', False)
leaf = next(iter(self.leaves()))
tree_mh = leaf.data.minhash
scaled = tree_mh.scaled
threshold_bp = kwargs.get('threshold_bp', 0.0)
threshold = 0.0
# are we setting a threshold?
if threshold_bp:
# if we have a threshold_bp of N, then that amounts to N/scaled
# hashes:
n_threshold_hashes = threshold_bp / scaled
# that then requires the following containment:
threshold = n_threshold_hashes / len(query.minhash)
# is it too high to ever match? if so, exit.
if threshold > 1.0:
return []
# actually do search!
results = []
for leaf in self.find(search_fn, query, threshold,
unload_data=unload_data):
leaf_mh = leaf.data.minhash
containment = query.minhash.contained_by(leaf_mh, True)
if containment >= threshold:
results.append((containment, leaf.data, None))
results.sort(key=lambda x: -x[0])
return results
def _rebuild_node(self, pos=0):
"""Recursively rebuilds an internal node (if it is not present).
Parameters
----------
pos: int
node to be rebuild. Any internal node under it will be rebuild too.
If you want to rebuild all missing internal nodes you can use pos=0
(the default).
"""
node = self._nodes.get(pos, None)
if node is not None:
# this node was already build, skip
return
node = Node(self.factory, name="internal.{}".format(pos))
self._nodes[pos] = node
for c in self.children(pos):
if c.pos in self._missing_nodes or isinstance(c.node, Leaf):
cnode = c.node
if cnode is None:
self._rebuild_node(c.pos)
cnode = self._nodes[c.pos]
cnode.update(node)
[docs] def parent(self, pos):
"""Return the parent of the node at position ``pos``.
If it is the root node (position 0), returns None.
Parameters
----------
pos: int
Position of the node in the tree.
Returns
-------
NodePos :
A NodePos namedtuple with the position and content of the parent node.
"""
if pos == 0:
return None
p = int(math.floor((pos - 1) / self.d))
if p in self._leaves:
return NodePos(p, self._leaves[p])
node = self._nodes.get(p, None)
return NodePos(p, node)
[docs] def children(self, pos):
"""Return all children nodes for node at position ``pos``.
Parameters
----------
pos: int
Position of the node in the tree.
Returns
-------
list of NodePos
A list of NodePos namedtuples with the position and content of all
children nodes.
"""
return [self.child(pos, c) for c in range(self.d)]
[docs] def child(self, parent, pos):
"""Return a child node at position ``pos`` under the ``parent`` node.
Parameters
----------
parent: int
Parent node position in the tree.
pos: int
Position of the child one under the parent. Ranges from
[0, arity - 1], where arity is the arity of the SBT
(usually it is 2, a binary tree).
Returns
-------
NodePos
A NodePos namedtuple with the position and content of the
child node.
"""
cd = self.d * parent + pos + 1
if cd in self._leaves:
return NodePos(cd, self._leaves[cd])
node = self._nodes.get(cd, None)
return NodePos(cd, node)
[docs] def save(self, path, storage=None, sparseness=0.0, structure_only=False):
"""Saves an SBT description locally and node data to a storage.
Parameters
----------
path : str
path to where the SBT description should be saved.
storage : Storage, optional
Storage to be used for saving node data.
Defaults to FSStorage (a hidden directory at the same level of path)
sparseness : float
How much of the internal nodes should be saved.
Defaults to 0.0 (save all internal nodes data),
can go up to 1.0 (don't save any internal nodes data)
structure_only: boolean
Write only the index schema and metadata, but not the data.
Defaults to False (save data too)
Returns
-------
str
full path to the new SBT description
"""
info = {}
info['d'] = self.d
info['version'] = 6
info["index_type"] = self.__class__.__name__ # TODO: check
# choose between ZipStorage and FS (file system/directory) storage.
if path.endswith(".sbt.zip"):
kind = "Zip"
storage = ZipStorage(path)
backend = "FSStorage"
name = os.path.basename(path[:-8])
subdir = '.sbt.{}'.format(name)
storage_args = FSStorage("", subdir).init_args()
storage.save(subdir + "/", b"")
index_filename = os.path.abspath(path)
else:
kind = "FS"
name = os.path.basename(path)
if path.endswith('.sbt.json'):
name = name[:-9]
index_filename = os.path.abspath(path)
else:
index_filename = os.path.abspath(path + '.sbt.json')
if storage is None:
# default storage
location = os.path.dirname(index_filename)
subdir = '.sbt.{}'.format(name)
storage = FSStorage(location, subdir)
index_filename = os.path.join(location, index_filename)
backend = [k for (k, v) in STORAGES.items() if v == type(storage)][0]
storage_args = storage.init_args()
info['storage'] = {
'backend': backend,
'args': storage_args
}
info['factory'] = {
'class': GraphFactory.__name__,
'args': self.factory.init_args()
}
nodes = {}
leaves = {}
total_nodes = len(self)
for n, (i, node) in enumerate(self):
if node is None:
continue
if isinstance(node, Node):
if random() - sparseness <= 0:
continue
data = {
# TODO: start using md5sum instead?
'filename': os.path.basename(node.name),
'name': node.name
}
try:
node.metadata.pop('max_n_below')
except (AttributeError, KeyError):
pass
data['metadata'] = node.metadata
if structure_only is False:
# trigger data loading before saving to the new place
node.data
node.storage = storage
if kind == "Zip":
node.save(os.path.join(subdir, data['filename']))
elif kind == "FS":
data['filename'] = node.save(data['filename'])
if isinstance(node, Node):
nodes[i] = data
else:
leaves[i] = data
if n % 100 == 0:
notify("{} of {} nodes saved".format(n+1, total_nodes), end='\r')
notify("\nFinished saving nodes, now saving SBT json file.")
info['nodes'] = nodes
info['signatures'] = leaves
if kind == "Zip":
tree_data = json.dumps(info).encode("utf-8")
save_path = "{}.sbt.json".format(name)
storage.save(save_path, tree_data)
storage.close()
elif kind == "FS":
with open(index_filename, 'w') as fp:
json.dump(info, fp)
notify("\nFinished saving SBT, available at {0}\n".format(index_filename))
return path
[docs] @classmethod
def load(cls, location, leaf_loader=None, storage=None, print_version_warning=True):
"""Load an SBT description from a file.
Parameters
----------
location : str
path to the SBT description.
leaf_loader : function, optional
function to load leaf nodes. Defaults to ``Leaf.load``.
storage : Storage, optional
Storage to be used for saving node data.
Defaults to FSStorage (a hidden directory at the same level of path)
Returns
-------
SBT
the SBT tree built from the description.
"""
tempfile = None
sbt_name = None
tree_data = None
if storage is None and ZipStorage.can_open(location):
storage = ZipStorage(location)
sbts = storage.list_sbts()
if len(sbts) != 1:
print("no SBT, or too many SBTs!")
else:
tree_data = storage.load(sbts[0])
tempfile = NamedTemporaryFile()
tempfile.write(tree_data)
tempfile.flush()
dirname = os.path.dirname(tempfile.name)
sbt_name = os.path.basename(tempfile.name)
if sbt_name is None:
dirname = os.path.dirname(os.path.abspath(location))
sbt_name = os.path.basename(location)
if sbt_name.endswith('.sbt.json'):
sbt_name = sbt_name[:-9]
sbt_fn = os.path.join(dirname, sbt_name)
if not sbt_fn.endswith('.sbt.json') and tempfile is None:
sbt_fn += '.sbt.json'
with open(sbt_fn) as fp:
jnodes = json.load(fp)
if tempfile is not None:
tempfile.close()
version = 1
if isinstance(jnodes, Mapping):
version = jnodes['version']
if leaf_loader is None:
leaf_loader = Leaf.load
loaders = {
1: cls._load_v1,
2: cls._load_v2,
3: cls._load_v3,
4: cls._load_v4,
5: cls._load_v5,
6: cls._load_v6,
}
try:
loader = loaders[version]
except KeyError:
raise IndexNotSupported()
#if version >= 6:
# if jnodes.get("index_type", "SBT") == "LocalizedSBT":
# loaders[6] = LocalizedSBT._load_v6
if version < 3 and storage is None:
storage = FSStorage(dirname, '.sbt.{}'.format(sbt_name))
elif storage is None:
klass = STORAGES[jnodes['storage']['backend']]
if jnodes['storage']['backend'] == "FSStorage":
storage = FSStorage(dirname, jnodes['storage']['args']['path'])
elif storage is None:
storage = klass(**jnodes['storage']['args'])
return loader(jnodes, leaf_loader, dirname, storage, print_version_warning)
@staticmethod
def _load_v1(jnodes, leaf_loader, dirname, storage, print_version_warning=True):
if jnodes[0] is None:
raise ValueError("Empty tree!")
sbt_nodes = {}
sample_bf = os.path.join(dirname, jnodes[0]['filename'])
ksize, tablesize, ntables = extract_nodegraph_info(sample_bf)[:3]
factory = GraphFactory(ksize, tablesize, ntables)
for i, jnode in enumerate(jnodes):
if jnode is None:
continue
jnode['filename'] = os.path.join(dirname, jnode['filename'])
if 'internal' in jnode['name']:
jnode['factory'] = factory
sbt_node = Node.load(jnode, storage)
else:
sbt_node = leaf_loader(jnode, storage)
sbt_nodes[i] = sbt_node
tree = SBT(factory)
tree._nodes = sbt_nodes
return tree
@classmethod
def _load_v2(cls, info, leaf_loader, dirname, storage, print_version_warning=True):
nodes = {int(k): v for (k, v) in info['nodes'].items()}
if nodes[0] is None:
raise ValueError("Empty tree!")
sbt_nodes = {}
sbt_leaves = {}
sample_bf = os.path.join(dirname, nodes[0]['filename'])
k, size, ntables = extract_nodegraph_info(sample_bf)[:3]
factory = GraphFactory(k, size, ntables)
for k, node in nodes.items():
if node is None:
continue
node['filename'] = os.path.join(dirname, node['filename'])
if 'internal' in node['name']:
node['factory'] = factory
sbt_node = Node.load(node, storage)
sbt_nodes[k] = sbt_node
else:
sbt_node = leaf_loader(node, storage)
sbt_leaves[k] = sbt_node
tree = cls(factory, d=info['d'])
tree._nodes = sbt_nodes
tree._leaves = sbt_leaves
return tree
@classmethod
def _load_v3(cls, info, leaf_loader, dirname, storage, print_version_warning=True):
nodes = {int(k): v for (k, v) in info['nodes'].items()}
if not nodes:
raise ValueError("Empty tree!")
sbt_nodes = {}
sbt_leaves = {}
factory = GraphFactory(*info['factory']['args'])
max_node = 0
for k, node in nodes.items():
if node is None:
continue
if 'internal' in node['name']:
node['factory'] = factory
sbt_node = Node.load(node, storage)
sbt_nodes[k] = sbt_node
else:
sbt_node = leaf_loader(node, storage)
sbt_leaves[k] = sbt_node
max_node = max(max_node, k)
tree = cls(factory, d=info['d'], storage=storage)
tree._nodes = sbt_nodes
tree._leaves = sbt_leaves
tree._missing_nodes = {i for i in range(max_node)
if i not in sbt_nodes and i not in sbt_leaves}
if print_version_warning:
error("WARNING: this is an old index version, please run `sourmash migrate` to update it.")
error("WARNING: proceeding with execution, but it will take longer to finish!")
tree._fill_min_n_below()
return tree
@classmethod
def _load_v4(cls, info, leaf_loader, dirname, storage, print_version_warning=True):
nodes = {int(k): v for (k, v) in info['nodes'].items()}
if not nodes:
raise ValueError("Empty tree!")
sbt_nodes = {}
sbt_leaves = {}
factory = GraphFactory(*info['factory']['args'])
max_node = 0
for k, node in nodes.items():
if 'internal' in node['name']:
node['factory'] = factory
sbt_node = Node.load(node, storage)
sbt_nodes[k] = sbt_node
else:
sbt_node = leaf_loader(node, storage)
sbt_leaves[k] = sbt_node
max_node = max(max_node, k)
tree = cls(factory, d=info['d'], storage=storage)
tree._nodes = sbt_nodes
tree._leaves = sbt_leaves
tree._missing_nodes = {i for i in range(max_node)
if i not in sbt_nodes and i not in sbt_leaves}
tree.next_node = max_node
return tree
@classmethod
def _load_v5(cls, info, leaf_loader, dirname, storage, print_version_warning=True):
nodes = {int(k): v for (k, v) in info['nodes'].items()}
leaves = {int(k): v for (k, v) in info['leaves'].items()}
if not leaves:
raise ValueError("Empty tree!")
sbt_nodes = {}
sbt_leaves = {}
if storage is None:
klass = STORAGES[info['storage']['backend']]
if info['storage']['backend'] == "FSStorage":
storage = FSStorage(dirname, info['storage']['args']['path'])
elif storage is None:
storage = klass(**info['storage']['args'])
factory = GraphFactory(*info['factory']['args'])
max_node = 0
for k, node in nodes.items():
node['factory'] = factory
sbt_node = Node.load(node, storage)
sbt_nodes[k] = sbt_node
max_node = max(max_node, k)
for k, node in leaves.items():
sbt_leaf = leaf_loader(node, storage)
sbt_leaves[k] = sbt_leaf
max_node = max(max_node, k)
tree = cls(factory, d=info['d'], storage=storage)
tree._nodes = sbt_nodes
tree._leaves = sbt_leaves
tree._missing_nodes = {i for i in range(max_node)
if i not in sbt_nodes and i not in sbt_leaves}
return tree
@classmethod
def _load_v6(cls, info, leaf_loader, dirname, storage, print_version_warning=True):
nodes = {int(k): v for (k, v) in info['nodes'].items()}
leaves = {int(k): v for (k, v) in info['signatures'].items()}
if not leaves:
raise ValueError("Empty tree!")
sbt_nodes = {}
sbt_leaves = {}
if storage is None:
klass = STORAGES[info['storage']['backend']]
if info['storage']['backend'] == "FSStorage":
storage = FSStorage(dirname, info['storage']['args']['path'])
elif storage is None:
storage = klass(**info['storage']['args'])
factory = GraphFactory(*info['factory']['args'])
max_node = 0
for k, node in nodes.items():
node['factory'] = factory
sbt_node = Node.load(node, storage)
sbt_nodes[k] = sbt_node
max_node = max(max_node, k)
for k, node in leaves.items():
sbt_leaf = leaf_loader(node, storage)
sbt_leaves[k] = sbt_leaf
max_node = max(max_node, k)
tree = cls(factory, d=info['d'], storage=storage)
tree._nodes = sbt_nodes
tree._leaves = sbt_leaves
tree._missing_nodes = {i for i in range(max_node)
if i not in sbt_nodes and i not in sbt_leaves}
return tree
def _fill_min_n_below(self):
"""\
Propagate the smallest hash size below each node up the tree from
the leaves.
"""
def fill_min_n_below(node, *args, **kwargs):
original_min_n_below = node.metadata.get('min_n_below', sys.maxsize)
min_n_below = original_min_n_below
children = kwargs['children']
for child in children:
if child.node is not None:
if isinstance(child.node, Leaf):
min_n_below = min(len(child.node.data.minhash), min_n_below)
else:
child_n = child.node.metadata.get('min_n_below', sys.maxsize)
min_n_below = min(child_n, min_n_below)
if min_n_below == 0:
min_n_below = 1
node.metadata['min_n_below'] = min_n_below
return original_min_n_below != min_n_below
self._fill_up(fill_min_n_below)
def _fill_internal(self):
def fill_nodegraphs(node, *args, **kwargs):
children = kwargs['children']
for child in children:
if child.node is not None:
child.node.update(node)
return True
self._fill_up(fill_nodegraphs)
def _fill_up(self, search_fn, *args, **kwargs):
visited, queue = set(), list(reversed(sorted(self._leaves.keys())))
debug("started filling up")
processed = 0
while queue:
node_p = queue.pop(0)
parent = self.parent(node_p)
if parent is None:
# we are in the root, no more nodes available to search
assert len(queue) == 0
return
was_missing = False
if parent.node is None:
if parent.pos in self._missing_nodes:
self._rebuild_node(parent.pos)
parent = self.parent(node_p)
was_missing = True
else:
continue
siblings = self.children(parent.pos)
if node_p not in visited:
visited.add(node_p)
for sibling in siblings:
visited.add(sibling.pos)
try:
queue.remove(sibling.pos)
except ValueError:
pass
if search_fn(parent.node, children=siblings, *args) or was_missing:
queue.append(parent.pos)
processed += 1
if processed % 100 == 0:
debug("processed {}, in queue {}", processed, len(queue), sep='\r')
def __len__(self):
internal_nodes = set(self._nodes).union(self._missing_nodes)
return len(internal_nodes) + len(self._leaves)
[docs] def print_dot(self):
print("""
digraph G {
nodesep=0.3;
ranksep=0.2;
margin=0.1;
node [shape=ellipse];
edge [arrowsize=0.8];
""")
for i, node in self._nodes.items():
if isinstance(node, Node):
print('"{}" [shape=box fillcolor=gray style=filled]'.format(
node.name))
for j, child in self.children(i):
if child is not None:
print('"{}" -> "{}"'.format(node.name, child.name))
print("}")
[docs] def print(self):
visited, stack = set(), [0]
while stack:
node_p = stack.pop()
node_g = self._nodes.get(node_p, None)
if node_p not in visited and node_g is not None:
visited.add(node_p)
depth = int(math.floor(math.log(node_p + 1, self.d)))
print(" " * 4 * depth, node_g)
if isinstance(node_g, Node):
stack.extend(c.pos for c in self.children(node_p)
if c.pos not in visited)
def __iter__(self):
for i, node in self._nodes.items():
yield (i, node)
for i, node in self._leaves.items():
yield (i, node)
def _parents(self, pos=0):
if pos == 0:
yield None
else:
p = self.parent(pos)
while p is not None:
yield p.pos
p = self.parent(p.pos)
[docs] def leaves(self, with_pos=False):
for pos, data in self._leaves.items():
if with_pos:
yield (pos, data)
else:
yield data
[docs] def combine(self, other):
larger, smaller = self, other
if len(other) > len(self):
larger, smaller = other, self
n = Node(self.factory, name="internal.0", storage=self.storage)
larger._nodes[0].update(n)
smaller._nodes[0].update(n)
new_nodes = {}
new_nodes[0] = n
new_leaves = {}
levels = int(math.ceil(math.log(len(larger), self.d))) + 1
current_pos = 1
n_previous = 0
n_next = 1
for level in range(1, levels + 1):
for tree in (larger, smaller):
for pos in range(n_previous, n_next):
if tree._nodes.get(pos, None) is not None:
new_node = copy(tree._nodes[pos])
new_node.name = "internal.{}".format(current_pos)
new_nodes[current_pos] = new_node
elif tree._leaves.get(pos, None) is not None:
new_node = copy(tree._leaves[pos])
new_leaves[current_pos] = new_node
current_pos += 1
n_previous = n_next
n_next = n_previous + int(self.d ** level)
current_pos = n_next
# TODO: do we want to return a new tree, or merge into this one?
self._nodes = new_nodes
self._leaves = new_leaves
return self
[docs]class Node(object):
"Internal node of SBT."
def __init__(self, factory, name=None, path=None, storage=None):
self.name = name
self.storage = storage
self._factory = factory
self._data = None
self._path = path
self.metadata = dict()
def __str__(self):
return '*Node:{name} [occupied: {nb}, fpr: {fpr:.2}]'.format(
name=self.name, nb=self.data.n_occupied(),
fpr=calc_expected_collisions(self.data, True, 1.1))
[docs] def save(self, path):
buf = self.data.to_bytes(compression=1)
return self.storage.save(path, buf)
@property
def data(self):
if self._data is None:
if self._path is None:
self._data = self._factory()
else:
data = self.storage.load(self._path)
self._data = Nodegraph.from_buffer(data)
return self._data
@data.setter
def data(self, new_data):
self._data = new_data
[docs] def unload(self):
self._data = None
[docs] @staticmethod
def load(info, storage=None):
new_node = Node(info['factory'],
name=info['name'],
path=info['filename'],
storage=storage)
new_node.metadata = info.get('metadata', {})
return new_node
[docs] def update(self, parent):
parent.data.update(self.data)
if 'min_n_below' in self.metadata:
min_n_below = min(parent.metadata.get('min_n_below', sys.maxsize),
self.metadata.get('min_n_below'))
if min_n_below == 0:
min_n_below = 1
parent.metadata['min_n_below'] = min_n_below
[docs]class Leaf(object):
def __init__(self, metadata, data=None, name=None, storage=None, path=None):
self.metadata = metadata
if name is None:
name = metadata
self.name = name
self.storage = storage
self._data = data
self._path = path
def __str__(self):
return '**Leaf:{name} [occupied: {nb}, fpr: {fpr:.2}] -> {metadata}'.format(
name=self.name, metadata=self.metadata,
nb=self.data.n_occupied(),
fpr=calc_expected_collisions(self.data, True, 1.1))
@property
def data(self):
if self._data is None:
data = self.storage.load(self._path)
self._data = Nodegraph.from_buffer(data)
return self._data
@data.setter
def data(self, new_data):
self._data = new_data
[docs] def unload(self):
self._data = None
[docs] def save(self, path):
buf = self.data.to_bytes(compression=1)
return self.storage.save(path, buf)
[docs] def update(self, parent):
parent.data.update(self.data)
[docs] @classmethod
def load(cls, info, storage=None):
return cls(info['metadata'],
name=info['name'],
path=info['filename'],
storage=storage)
def filter_distance(filter_a, filter_b, n=1000):
"""
Compute a heuristic distance per bit between two Bloom filters.
Parameters
----------
filter_a : Nodegraph
filter_b : Nodegraph
n : int
Number of positions to compare (in groups of 8)
Returns
-------
float
The distance between both filters (from 0.0 to 1.0)
"""
from numpy import array
A = filter_a.graph.get_raw_tables()
B = filter_b.graph.get_raw_tables()
distance = 0
for q, p in zip(A, B):
a = array(q, copy=False)
b = array(p, copy=False)
for i in map(lambda x: randint(0, len(a)), range(n)):
distance += sum(map(int,
[not bool((a[i] >> j) & 1) ^ bool((b[i] >> j) & 1)
for j in range(8)]))
return distance / (8.0 * len(A) * n)
def convert_cmd(name, backend):
"Convert an SBT to use a different back end."
from .sbtmh import SigLeaf
options = backend.split('(')
backend = options.pop(0)
backend = backend.lower().strip("'")
if options:
print(options)
options = options[0].split(')')
options = [options.pop(0)]
#options = {}
else:
options = []
if backend.lower() in ('ipfs', 'ipfsstorage'):
backend = IPFSStorage
elif backend.lower() in ('redis', 'redisstorage'):
backend = RedisStorage
elif backend.lower() in ('tar', 'tarstorage'):
backend = TarStorage
elif backend.lower() in ('fs', 'fsstorage'):
backend = FSStorage
if options:
options = [os.path.dirname(options[0]), os.path.basename(options[0])]
else:
# this is the default for SBT v2
tag = '.sbt.' + os.path.basename(name)
if tag.endswith('.sbt.json'):
tag = tag[:-9]
path = os.path.dirname(name)
options = [path, tag]
else:
error('backend not recognized: {}'.format(backend))
with backend(*options) as storage:
sbt = SBT.load(name, leaf_loader=SigLeaf.load)
sbt.save(name, storage=storage)