def __init__(self, storage_path):
self.path = storage
self.storage = ZODB.FileStorage.FileStorage(self.path)
self.db = ZODB.DB(storage)
self.connection = self.db.open()
self.data_root = self.connection.root
if not root.__dict__['_root'].get('Genome') :
root.Genome = BTree()
if not root.__dict__['_root'].get('Sequence') :
root.Sequence = BTree()
if not root.__dict__['_root'].get('Source') :
root.Source = BTree()
def __init__(self, conn):
conn.add(self)
assert self._p_oid == ONE
self.users = BTree() # {uid -> user}
self.users_by_name = BTree() # {uname -> user}
self.uids = BTree() # {cert_der -> uid}
self.certs = Certs() # Cert, persistent wrapper for
# concatinated cert data
# Add nobody placeholder
with open(os.path.join(os.path.dirname(__file__), 'nobody.pem')) as f:
nobody_pem = f.read()
self.certs.add(nobody_pem)
self.uids[get_der(nobody_pem)] = None
def modifier(inq, outq): while True: item=inq.get() if item is None: break else: sequence_number=item[0] sequence=item[1].strip().lower() sequence = ''.join([i for i in sequence if i in allowed]) local_features = BTree() for i in range(len(sequence)-k+1): kmer = sequence[i:i+k] feature_index = features[kmer] index = feature_index try: current_count = local_features[index] local_features[index] = current_count + 1 except KeyError: local_features[index] = 1 #normalization sum = 0 for value in local_features.values(): sum = sum + (value*value) sum = math.sqrt(sum) for key in local_features.keys(): value = local_features[key] local_features[key] = float(value)/sum out_string = str(sequence_number) for key,val in local_features.iteritems(): out_string = out_string + ' ' + str(key) + '-' + str(val) out_string = out_string + '\n' outq.put(out_string) del local_features
def positivity(address): ################# read canopy output ################# positivity_scores = BTree() with open(address,'r') as f: for line in f: canopy_members = line.strip().split() total = float(len(canopy_members)) number_of_positive = 0 for member in canopy_members: if membertag_to_class(member) == 1: number_of_positive = number_of_positive + 1 p_score = float(number_of_positive)/total for member in canopy_members: try: score_list = positivity_scores[member] score_list.append(p_score) positivity_scores[member] = score_list except: positivity_scores[member] = [p_score] ############### take average score ################### for key in positivity_scores.keys(): score_list = positivity_scores[key] sum = float(0) for item in score_list: sum = sum + item final_score = sum/float(len(score_list)) positivity_scores[key] = final_score ################ prepare output #################### output = '' for key,value in positivity_scores.items(): output = output + key + ' ' + str(value) + '\n' with open(positivity_outputs, 'w') as f: f.write(output)
def get_students(conn):
root = conn.root
if 'students' not in conn.root():
print("init zodb database...")
students = BTree() # btree可以当成一个字典来用
root.students = students # 将students交给zodb来管理
else:
students = root.students
return students
def get_students(conn):
root = conn.root
if 'students' not in conn.root():
print("lala")
students = BTree() # btree可以当成一个字典来用
root.students = students # 将students交给zodb来管理
else:
print("haha")
students = root.students
return students
def __init__(self, resources: List = None, indexed=True):
self.internal = DictObject()
self.internal.resources = deque(resources or [])
self.internal.indexed = indexed
self.internal.indexes = defaultdict(BTree)
if indexed:
self.internal.indexes.update({
k: BTree()
for k, field in self.ravel.owner.Schema.fields.items()
if isinstance(field, self.ravel.indexed_field_types)
})
def test_write():
with tempfile.NamedTemporaryFile() as temp:
connection = make_storage(temp.name)
root = connection.root
root.nodes = BTree()
esp = ESPNode("esp8266")
root.nodes['first_esp'] = esp
water_sensor = Node("water_sensor")
root.nodes['water'] = water_sensor
e = Edge(esp, water_sensor)
print(esp.from_edges)
# root.nodes['wtf'] = e
# esp.from_edges.append(water_sensor)
transaction.commit()
def test(regressor, scaler):
test_samples = []
with open(os.path.join(os.getcwd(), "test_samples.txt"), "r") as f:
line = f.readline()
test_samples = line.strip().split()
for sample in test_samples:
regressed_values = BTree()
test_row = []
test_col = []
test_data = []
path = os.path.join(test_data_path, sample)
row_counter = 0
with open(path, 'r') as f:
for line in f:
line = line.strip()
if line != ' ' and line != '' and line != '\n':
items = line.split()
sequence_number = int(items[0].strip())
regressed_values[row_counter] = sequence_number
for i in items[1:]:
col_id, data_value = i.strip().split("-")
col_id = int(col_id)
data_value = float(data_value)
test_row.append(row_counter)
test_col.append(col_id)
test_data.append(data_value)
row_counter = row_counter + 1
test_row = np.asarray(test_row)
test_col = np.asarray(test_col)
test_data = np.asarray(test_data)
x_test = coo_matrix((test_data, (test_row, test_col)),
shape=(row_counter, total_cols))
x_test = scaler.transform(x_test)
y_test = regressor.predict(x_test)
y_test[y_test < 0] = 0.0
y_test[y_test > 1] = 1.0
toBeSorted = []
for r in range(0, len(y_test)):
toBeSorted.append((regressed_values[r], y_test[r]))
toBeSorted = sorted(toBeSorted, key=lambda x: x[1], reverse=True)
gc.collect()
out = os.path.join(regression_output_Path, sample)
with open(out, 'w') as f:
for value in toBeSorted:
f.write(str(value[0]) + ' ' + str(value[1]) + '\n')
def train_set_positivity():
data = BTree()
with open("positivity_outputs.txt", "r") as f:
for line in f:
line = line.strip().split()
sample = line[0].split("_")[0]
seq_number = line[0].split("_")[1]
score = line[1]
try:
curr = data[sample]
curr.append((seq_number,score))
data[sample] = curr
except:
data[sample] = [(seq_number,score)]
for key, val in data.items():
with open(os.path.join(train_set_positivity_scores, key), "w") as f:
for item in val:
f.write(item[0] + " " + item[1] + "\n")
def test_jsonifier(self):
from zope.testing.loggingsupport import InstalledHandler
handler = InstalledHandler('newt.db.jsonpickle')
from ..jsonpickle import Jsonifier
jsonifier = Jsonifier()
p, tid = self.conn._storage.load(z64)
class_name, ghost_pickle, state = jsonifier('0', p)
self.assertEqual('persistent.mapping.PersistentMapping', class_name)
self.assertEqual('{"data": {}}', state)
self.assertTrue(
p.startswith(ghost_pickle) and ghost_pickle[-1:] == b'.'
and b'persistent.mapping' in ghost_pickle)
# custon skip_class
jsonifier2 = Jsonifier(skip_class=lambda c: 1)
self.assertEqual((None, None, None), jsonifier2('0', p))
# empty records are skipped:
self.assertEqual((None, None, None), jsonifier('0', ''))
# BTrees are skipped by default
from BTrees.OOBTree import BTree
self.root.x = BTree()
self.conn.transaction_manager.commit()
p, tid = self.conn._storage.load(self.root.x._p_oid)
self.assertEqual((None, None, None), jsonifier('0', p))
# errors are logged, and return Nones:
self.assertEqual(handler.records, [])
self.assertEqual((None, None, None), jsonifier('foo', b'badness'))
self.assertEqual(
[r.getMessage().replace("b'", "'") for r in handler.records],
["Failed pickle load, oid: 'foo', pickle starts: 'badness'"])
handler.uninstall()
def __init__(self):
self.contracts = BTree()
self.instance_lists = BTree()
self.last_block = 0
import math
####################################################################
import multiprocessing
from multiprocessing import Pool
from multiprocessing import Process
from multiprocessing import Queue
####################################################################
d = 1000
####################################################################
hyper_planes = BTree()
ft_count = 0
kmer_feature_file = os.path.join(os.getcwd(), "kmer_feat_index.txt")
with open(kmer_feature_file, 'r') as f:
for line in f:
line = line.strip()
if line != '' and line != ' ' and line != '\n':
line = line.split()[1]
ft_count = ft_count + 1
try:
index = int(line)
hyper_planes[index] = np.random.randn(d)
except ValueError:
print('\nkmer feature index not valid\n')
sys.exit(1)
if index != ft_count - 1:
def on_bind(self, resource_type: Type['Resource'], **kwargs):
for k, field in resource_type.ravel.schema.fields.items():
if field.scalar and (type(field) is not Field):
self.indexes[k] = BTree()
def main():
hash_file = os.path.join("combined_train_lsh.txt")
canopy_output_file = os.path.join("canopy_output.txt")
hashed_sequence = BTree()
current_dataset = []
dataCounter = 0
with open(hash_file, 'r') as f:
for line in f:
line = line.strip().split()
try:
seq_id = line[0]
hash_value = (int)(line[1])
current_dataset.append(seq_id)
dataCounter = dataCounter + 1
hashed_sequence[seq_id] = hash_value
except ValueError:
print("\nhash value invalid\n")
sys.exit(1)
print("total # of instances in training set: " + str(dataCounter))
gc.collect()
time.sleep(2)
canopy_counter = 0
with open(canopy_output_file, 'w') as f:
while dataCounter > 0:
canopyIndex = random.choice(current_dataset)
canopyCenterHash = hashed_sequence[canopyIndex]
current_dataset.remove(canopyIndex)
dataCounter = dataCounter - 1
remove_list = []
####################################################################
num_workers = (int)(multiprocessing.cpu_count() / 2)
workers = []
inq = multiprocessing.Queue()
outq = multiprocessing.Queue()
deleteq = multiprocessing.Queue()
st_curr_canopy_q = multiprocessing.Queue()
current_dataset_q = multiprocessing.Queue()
####################################################################
for i in range(num_workers):
tmp = multiprocessing.Process(target=modifier,
args=(
canopyCenterHash,
inq,
outq,
deleteq,
))
tmp.daemon = True
tmp.start()
workers.append(tmp)
####################################################################
fileWriteProcess = multiprocessing.Process(target=writer,
args=(
canopyIndex,
outq,
st_curr_canopy_q,
))
fileWriteProcess.daemon = True
fileWriteProcess.start()
####################################################################
for seq in current_dataset:
hv = hashed_sequence[seq]
inq.put((seq, hv))
####################################################################
datasetUpdater = multiprocessing.Process(target=data_updater,
args=(
current_dataset,
deleteq,
current_dataset_q,
))
datasetUpdater.daemon = True
datasetUpdater.start()
####################################################################
for i in range(num_workers):
inq.put(None)
for worker in workers:
worker.join()
####################################################################
outq.put(None)
deleteq.put(None)
while True:
try:
curr_canopy = st_curr_canopy_q.get()
break
except:
pass
f.write(curr_canopy)
fileWriteProcess.join()
while True:
try:
c_s = current_dataset_q.get()
current_dataset = c_s
break
except:
pass
datasetUpdater.join()
####################################################################
# with open("temp_file.txt", "r") as t:
# s = t.readline()
# f.write(s)
####################################################################
inq.close()
outq.close()
#final_output_string.close()
deleteq.close()
del inq
del outq
#del final_output_string
del deleteq
del workers
gc.collect()
####################################################################
dataCounter = len(current_dataset)
canopy_counter = canopy_counter + 1
print(
str(canopy_counter) + ". Remaining # of instances: " +
str(dataCounter))
def __init__(self, invertedIndex):
self._invertedIndex = invertedIndex
self._btree = BTree()
self.build()
all_samples.extend(filenames)
break
train_source_data = os.path.join(os.getcwd(), "train_set_positivity_scores")
train_samples = []
for (dirpath, dirnames, filenames) in walk(train_source_data):
train_samples.extend(filenames)
break
test_source_data = os.path.join(os.getcwd(), "regression_output")
test_samples = []
for (dirpath, dirnames, filenames) in walk(test_source_data):
test_samples.extend(filenames)
break
sampleClassLabels = BTree()
####################################################################
def classLabelCreator():
global all_samples
global sampleClassLabels
global metadata
for s in all_samples:
sampleClassLabels[s] = 0
with open(metadata, 'r') as mf:
for line in mf:
line = line.strip().split()
if ("CD" in line[0]) or ("UC" in line[0]):
for key in sampleClassLabels.keys():
def __init__(self):
super().__init__()
self._btree = BTree()
self.build()
def _get_token_map(self):
acl_users = self.getSite().acl_users.aq_base
if not hasattr(acl_users, '_recover_password_tokens'):
acl_users._recover_password_tokens = BTree()
return acl_users._recover_password_tokens
import multiprocessing
from multiprocessing import Pool
from multiprocessing import Process
from multiprocessing import Queue
####################################################################
k = 6
####################################################################
kmer_index_file = os.path.join(os.getcwd(),'kmer_feat_index.txt')
f = open(kmer_index_file, 'w')
allowed = ['a','c','g','t']
features = BTree()
feature_count = -1
for ft_string in map(''.join, itertools.product('acgt', repeat=k)):
feature_count = feature_count + 1
features[ft_string]=feature_count
f.write(ft_string + ' ' + str(feature_count) + '\n')
f.close()
####################################################################
def reader(input_file, inq):
sequence_number=0
f = open(input_file, 'r')
sequence=''
for line in f:
line=line.strip()
# In the code below, we track the number of requests made and the reset time.
# When we hit the limit, we pause until the reset time and then continue.
github = github3.login(github_login, github_password)
calls_left = api_calls_left(github)
msg('Started at ', datetime.now())
started = timer()
msg('Opening database "{}"'.format(dbfile))
dbconnection = ZODB.connection(dbfile)
dbroot = dbconnection.root()
if not 'github' in dbroot.keys():
msg('Empty database -- creating root object')
dbroot['github'] = BTree()
else:
print('"{}" contains {} entries'.format(dbfile, len(dbroot['github'])))
db = dbroot['github']
msg('Initial GitHub API calls remaining: ', calls_left)
msg('Generating list of all repositories:')
# If we're restarting this process, we will already have entries in the db.
count = len(dbroot['github'])
# The iterator returned by github.all_repositories() is continuous; behind
# the scenes, it uses the GitHub API to get new data when needed. Each API
# call nets 100 repository records, so after we go through 100 objects in the
import time
import math
####################################################################
import multiprocessing
from multiprocessing import Pool
from multiprocessing import Process
from multiprocessing import Queue
####################################################################
positivity_score_output = os.path.join(os.getcwd(), "positivity_outputs.txt")
nn_train_file = os.path.join(os.getcwd(), "nn_train_file.txt")
kmer_file = os.path.join(os.getcwd(), "metagenomic_data_kmer")
scores = BTree()
def read_scores():
with open(positivity_score_output, "r") as f:
for line in f:
line = line.strip().split()
sample_sequence = line[0]
sample = sample_sequence.split("_")[0]
sequence = (int)(sample_sequence.split("_")[1])
score = (float)(line[1])
try:
current = scores[sample]
current.append((sequence, score))
scores[sample] = current
except:
import sys import itertools import time import math #################################################################### import multiprocessing from multiprocessing import Pool from multiprocessing import Process from multiprocessing import Queue from Cython.Shadow import address #################################################################### sampleClassLabels = BTree() train_instances = BTree() metadata = os.path.join(os.getcwd(),"metadata.txt") kmer_data_path = os.path.join(os.getcwd(), "metagenomic_data_kmer") canopy_output = os.path.join(os.getcwd(), "canopy_output.txt") positivity_outputs = os.path.join(os.getcwd(), "positivity_outputs.txt") train_set_positivity_scores = os.path.join(os.getcwd(), "train_set_positivity_scores") samples = [] for (dirpath, dirnames, filenames) in walk(kmer_data_path): samples.extend(filenames) break #################################################################### def classLabelCreator(): global samples
def _prepareDataBase(objName, dbPosition): # type: (str, Persistent)-> None
if objName not in dbPosition:
dbPosition[objName] = BTree()
import transaction
class Student:
def __init__(self, id, name, class_name, score):
self.id = id
self.name = name
self.class_name = class_name
self.score = score
csv_filename = 'data.csv'
db_filename = 'data/data.fs'
conn = ZODB.connection(db_filename)
students = BTree()
root = conn.root
root.students = students
with open(csv_filename, mode="r", encoding="GBK") as file:
students.clear()
reader = csv.reader(file)
for row in reader:
id = row[0]
name = row[1]
class_name = row[2]
score = float(row[3])
if id in students.keys():
print(f"载入失败：学号{id}已存在！")
student = Student(id, name, class_name, score)
def __init__(self):
self.T = BTree()