#!/usr/bin/env python
"""
An implementation of sequence bloom trees, Solomon & Kingsford, 2015.
"""
from collections import namedtuple, Counter
from collections.abc import Mapping
from copy import copy
import json
import math
import os
from random import randint, random
import sys
from tempfile import NamedTemporaryFile
from cachetools import Cache
from io import StringIO
from .exceptions import IndexNotSupported
from .sbt_storage import FSStorage, IPFSStorage, RedisStorage, ZipStorage
from .logging import error, notify, debug
from .index import Index, IndexSearchResult, CollectionManifest
from .picklist import passes_all_picklists
from .nodegraph import Nodegraph, extract_nodegraph_info, calc_expected_collisions
STORAGES = {
'FSStorage': FSStorage,
'IPFSStorage': IPFSStorage,
'RedisStorage': RedisStorage,
'ZipStorage': ZipStorage,
}
NodePos = namedtuple("NodePos", ["pos", "node"])
[docs]
class GraphFactory:
"""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)
class _NodesCache(Cache):
"""A cache for SBT nodes that calls .unload() when the node is removed from cache.
This is adapted from the LFU cache in https://github.com/tkem/cachetools,
but removing the largest node ids first (those near the bottom/leaves of
the SBT).
"""
def __init__(self, maxsize, getsizeof=None):
Cache.__init__(self, maxsize, getsizeof)
self.__counter = Counter()
def __getitem__(self, key, cache_getitem=Cache.__getitem__):
value = cache_getitem(self, key)
self.__counter[key] -= 1
return value
def __setitem__(self, key, value, cache_setitem=Cache.__setitem__):
cache_setitem(self, key, value)
self.__counter[key] -= 1
def __delitem__(self, key, cache_delitem=Cache.__delitem__):
cache_delitem(self, key)
del self.__counter[key]
def popitem(self):
"""Remove and return the `(key, value)` pair least recently used."""
try:
# Select least frequently used keys,
# limit to 50 items to avoid dealing with huge lists
common = self.__counter.most_common()[:50]
# common might include different values, so let's use
# only keys that have the same value as the first one
# (all those with the same count are least frequently used items)
count = common[0][1]
# we want to remove the item closest to the leaves,
# and since node ids increase as they get farther from the root
# we just need to select the maximum key/node id
(key, _) = max(c for c in common if c[1] == count)
except IndexError:
msg = '%s is empty' % self.__class__.__name__
raise KeyError(msg) from None
else:
value = self.pop(key)
value.unload()
return (key, value)
[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.
cache_size: int, default None
Number of internal nodes to cache in memory.
If set to None, will not remove any nodes from memory
(cache grows without bounds).
Notes
-----
We use two dicts to store the tree structure: One for the internal nodes,
and another for the leaves (datasets).
"""
is_database = True
def __init__(self, factory, *, d=2, storage=None, cache_size=None):
self.factory = factory
self._nodes = {}
self._missing_nodes = set()
self._leaves = {}
self.d = d
self.next_node = 0
self.storage = storage
if cache_size is None:
cache_size = sys.maxsize
self._nodescache = _NodesCache(maxsize=cache_size)
self._location = None
self.picklists = []
self.manifest = None
@property
def location(self):
return self._location
[docs]
def signatures(self):
if self.manifest:
# if manifest, use it & load using direct path to storage.
# this will be faster when using picklists.
from .signature import load_one_signature
manifest = self.manifest
# iteratively select picklists; no other selection criteria
# apply to SBTs, since ksize etc are fixed as part of indexing.
for picklist in self.picklists:
manifest = manifest.select_to_manifest(picklist=picklist)
for loc in manifest.locations():
buf = self.storage.load(loc)
# if more than one signature can be in a file, we need
# to recheck picklists here.
ss = load_one_signature(buf)
yield ss
else:
# no manifest? iterate over all leaves.
for k in self.leaves():
ss = k.data
if passes_all_picklists(ss, self.picklists):
yield ss
def _signatures_with_internal(self):
"""Return an iterator of tuples (ss, storage_path, internal_location).
Note: does not limit signatures to subsets.
"""
for k in self.leaves():
ss = k.data
yield ss, k._path
[docs]
def select(self, ksize=None, moltype=None, num=0, scaled=0,
containment=False, abund=None, picklist=None):
"""Make sure this database matches the requested requirements.
Will always raise ValueError if a requirement cannot be met.
The only tricky bit here is around downsampling: if the scaled
value being requested is higher than the signatures in the
SBT, we can use the SBT for containment but not for
similarity. This is because:
* if we are doing containment searches, the intermediate nodes
can still be used for calculating containment of signatures
with higher scaled values. This is because only hashes that match
in the higher range are used for containment scores.
* however, for similarity, _all_ hashes are used, and we cannot
implicitly downsample or necessarily estimate similarity if
the scaled values differ.
"""
# pull out a signature from this collection -
first_sig = next(iter(self.signatures()))
db_mh = first_sig.minhash
# check ksize.
if ksize is not None and db_mh.ksize != ksize:
raise ValueError(f"search ksize {ksize} is different from database ksize {db_mh.ksize}")
# check moltype.
if moltype is not None and db_mh.moltype != moltype:
raise ValueError(f"search moltype {moltype} is different from database moltype {db_mh.moltype}")
# containment requires 'scaled'.
if containment:
if not scaled:
raise ValueError("'containment' requires 'scaled' in SBT.select'")
if not db_mh.scaled:
raise ValueError("cannot search this SBT for containment; signatures are not calculated with scaled")
# 'num' and 'scaled' do not mix.
if num:
if not db_mh.num:
raise ValueError(f"this database was created with 'scaled' MinHash sketches, not 'num'")
if num != db_mh.num:
raise ValueError(f"num mismatch for SBT: num={num}, {db_mh.num}")
if scaled:
if not db_mh.scaled:
raise ValueError(f"this database was created with 'num' MinHash sketches, not 'scaled'")
# we can downsample SBTs for containment operations.
if scaled > db_mh.scaled and not containment:
raise ValueError(f"search scaled value {scaled} is less than database scaled value of {db_mh.scaled}")
if abund:
raise ValueError("SBT indices do not support sketches with abund=True")
if picklist is not None:
self.picklists.append(picklist)
if len(self.picklists) > 1:
raise ValueError("we do not (yet) support multiple picklists for SBTs")
return self
[docs]
def new_node_pos(self, node):
# note: node is not actually used in this function! CTB
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)
else:
# this branch should never be reached; put guard in to make sure!
assert 0
# 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)
def _find_nodes(self, search_fn, *args, **kwargs):
"Search the tree using `search_fn`."
unload_data = kwargs.get("unload_data", True)
# 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:
if node_p in self._nodescache:
node_g = self._nodescache[node_p]
else:
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
self._nodescache[node_p] = node_g
# 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 find(self, search_fn, query, **kwargs):
"""
Do a Jaccard similarity or containment search, yield results.
Here 'search_fn' should be an instance of 'JaccardSearch'.
Queries with higher scaled values than the database
can still be used for containment search, but not for similarity
search. See SBT.select(...) for details.
"""
from .sbtmh import SigLeaf
search_fn.check_is_compatible(query)
query_mh = query.minhash
# figure out downsampling using the first leaf in the tree --
a_leaf = next(iter(self.leaves()))
tree_scaled = a_leaf.data.minhash.scaled
# scaled?
if tree_scaled:
assert query_mh.scaled
# pick the larger scaled of the query & node
scaled = max(query_mh.scaled, tree_scaled)
if query_mh.scaled < tree_scaled:
query_mh = query_mh.downsample(scaled=tree_scaled)
# provide function to downsample leaf_node as well
if scaled == tree_scaled:
downsample_node = lambda x: x
else:
def downsample_node(node_mh):
return node_mh.downsample(scaled=scaled)
else:
assert query_mh.num
# pick the smaller num of the query & node
min_num = min(query_mh.num, a_leaf.data.minhash.num)
# downsample query once:
if query_mh.num > min_num:
query_mh = query_mh.downsample(num=min_num)
# provide function to downsample leaf nodes.
if min_num == a_leaf.data.minhash.num:
downsample_node = lambda x: x
else:
def downsample_node(node_mh):
return node_mh.downsample(num=min_num)
query_size = len(query_mh)
# store scores here so we don't need to recalculate
results = {}
# construct a function to pass into ._find_nodes; this function
# will be used to prune tree searches based on internal node scores,
# in addition to finding leaf nodes.
def node_search(node, *args, **kwargs):
is_leaf = False
# leaf node? downsample so we can do signature comparison.
if isinstance(node, SigLeaf):
is_leaf = True
subj_mh = downsample_node(node.data.minhash)
subj_size = len(subj_mh)
subj_mh = subj_mh.flatten()
assert not subj_mh.track_abundance
shared_size, total_size = query_mh.intersection_and_union_size(subj_mh)
else: # Node / Nodegraph by minhash comparison
# no downsampling needed --
shared_size = node.data.matches(query_mh)
subj_size = node.metadata.get('min_n_below', -1)
if subj_size == -1:
raise ValueError("ERROR: no min_n_below on this tree, cannot search.")
total_size = subj_size # approximate; do not collect
# calculate score (exact, if leaf; approximate, if not)
score = search_fn.score_fn(query_size,
shared_size,
subj_size,
total_size)
if search_fn.passes(score):
if is_leaf: # terminal node? keep.
if search_fn.collect(score, node.data):
results[node.data] = score
return True
else: # it's a good internal node, keep.
return True
return False
# & execute!
for n in self._find_nodes(node_search, **kwargs):
ss = n.data
# filter on picklists
if passes_all_picklists(ss, self.picklists):
yield IndexSearchResult(results[ss], ss, self.location)
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.
# default to ZipStorage, unless .sbt.json is specified in filename.
kind = None
if not path.endswith(".sbt.json"):
kind = "Zip"
if not path.endswith('.sbt.zip'):
path += '.sbt.zip'
storage = ZipStorage(path, mode="w")
backend = "FSStorage"
assert path[-8:] == '.sbt.zip'
name = os.path.basename(path[:-8])
# align the storage prefix with what we do for FSStorage, below.
subdir = '.sbt.{}'.format(name)
storage_args = FSStorage("", subdir, make_dirs=False).init_args()
storage.save(subdir + "/", b"")
storage.subdir = subdir
index_filename = os.path.abspath(path)
else: # path.endswith('.sbt.json')
assert path.endswith('.sbt.json')
name = os.path.basename(path)
name = name[:-9]
index_filename = os.path.abspath(path)
if storage is None:
kind = "FS"
# default storage
location = os.path.dirname(index_filename)
# align subdir names with what we do above for ZipStorage
subdir = '.sbt.{}'.format(name)
# when we go to default of FSStorage, use full location for
# storage, e.g. location/.sbt.{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 = {}
internal_nodes = set(self._nodes).union(self._missing_nodes)
total_nodes = len(self) + len(internal_nodes)
manifest_rows = []
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":
new_name = node.save(os.path.join(subdir, data['filename']))
assert new_name.startswith(subdir + '/')
# strip off prefix
new_name = new_name[len(subdir) + 1:]
data['filename'] = new_name
else:
data['filename'] = node.save(data['filename'])
if isinstance(node, Node):
nodes[i] = data
else:
leaves[i] = data
row = node.make_manifest_row(data['filename'])
if row:
manifest_rows.append(row)
if n % 100 == 0:
notify(f"{format(n+1)} of {format(total_nodes)} nodes saved", end='\r')
# now, save the index file and manifests.
#
# for zipfiles, they get saved in the zip file.
# for FSStorage, we use the storage.save function.
#
# for everything else (Redis, IPFS), the index gets saved locally.
# the nodes/leaves are saved/loaded from the datatabase, and
# the index is used to get their names for loading.
# (CTB: manifests are not yet supported for Redis and IPFS)
#
notify("Finished saving nodes, now saving SBT index file.")
info['nodes'] = nodes
info['signatures'] = leaves
# finish constructing manifest object & save
manifest = CollectionManifest(manifest_rows)
manifest_name = f"{name}.manifest.csv"
manifest_fp = StringIO()
manifest.write_to_csv(manifest_fp, write_header=True)
manifest_data = manifest_fp.getvalue().encode("utf-8")
if kind == "Zip":
manifest_name = os.path.join(storage.subdir, manifest_name)
manifest_path = storage.save(manifest_name, manifest_data,
overwrite=True, compress=True)
elif kind == "FS":
manifest_name = manifest_name
manifest_path = storage.save(manifest_name, manifest_data,
overwrite=True)
else:
manifest_path = None
if manifest_path:
info['manifest_path'] = manifest_path
# now, save index.
tree_data = json.dumps(info).encode("utf-8")
if kind == "Zip":
save_path = "{}.sbt.json".format(name)
storage.save(save_path, tree_data, overwrite=True)
storage.flush()
elif kind == "FS":
storage.save(index_filename, tree_data, overwrite=True)
else:
# save tree locally.
with open(index_filename, 'wb') as tree_fp:
tree_fp.write(tree_data)
notify(f"Finished saving SBT index, available at {format(index_filename)}\n")
return path
[docs]
@classmethod
def load(cls, location, *, leaf_loader=None, storage=None, print_version_warning=True, cache_size=None):
"""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:
if ZipStorage.can_open(location):
storage = ZipStorage(location)
else:
if not location.endswith('.sbt.zip'):
location2 = location + '.sbt.zip'
if ZipStorage.can_open(location2):
storage = ZipStorage(location2)
if storage:
sbts = storage.list_sbts()
if len(sbts) == 1:
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'
try:
with open(sbt_fn) as fp:
jnodes = json.load(fp)
except NotADirectoryError as exc:
raise ValueError(str(exc))
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'])
obj = loader(jnodes, leaf_loader, dirname, storage, print_version_warning=print_version_warning, cache_size=cache_size)
obj._location = location
if 'manifest_path' in jnodes:
manifest_path = jnodes['manifest_path']
manifest_data = storage.load(manifest_path)
manifest_data = manifest_data.decode('utf-8')
manifest_fp = StringIO(manifest_data)
obj.manifest = CollectionManifest.load_from_csv(manifest_fp)
else:
obj.manifest = None
return obj
@staticmethod
def _load_v1(jnodes, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None):
if jnodes[0] is None:
raise ValueError("Empty tree!")
sbt_nodes = {}
sbt_leaves = {}
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)
sbt_nodes[i] = sbt_node
else:
sbt_node = leaf_loader(jnode, storage)
sbt_leaves[i] = sbt_node
tree = SBT(factory, cache_size=cache_size)
tree._nodes = sbt_nodes
tree._leaves = sbt_leaves
return tree
@classmethod
def _load_v2(cls, info, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None):
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'], cache_size=cache_size)
tree._nodes = sbt_nodes
tree._leaves = sbt_leaves
return tree
@classmethod
def _load_v3(cls, info, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None):
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, cache_size=cache_size)
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, cache_size=None):
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, cache_size=cache_size)
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, cache_size=None):
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, cache_size=cache_size)
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, cache_size=None):
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, cache_size=cache_size)
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):
return 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, unload_data=True):
for pos, data in self._leaves.items():
if with_pos:
yield (pos, data)
else:
yield data
if unload_data:
data.unload()
[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:
"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, overwrite=True)
@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):
if self.storage:
# Don't unload data if there is no Storage
# TODO: Check that data is actually in the storage?
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:
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))
[docs]
def make_manifest_row(self, location):
return None
@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):
if self.storage:
# Don't unload data if there is no Storage
# TODO: Check that data is actually in the storage?
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("'")
kwargs = {}
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 ('zip', 'zipstorage'):
backend = ZipStorage
kwargs['mode'] = 'w'
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, **kwargs) as storage:
sbt = SBT.load(name, leaf_loader=SigLeaf.load)
sbt.save(name, storage=storage)
