def __init__(self, species, download=False, withEns=True):
self.species = species
self.dbName = None
self.data = Collector(self.species)
self.data.collectAll(download=download, withEns=withEns)
self.TranscriptNoProteinRec = {}
self.DomainsSourceDB = 'DB_merged.sqlite'
self.DomainOrg = DomainOrganizer()
class Pusher:
def __init__ (self, config):
self.c1 = Collector(config.DB)
self.c2 = Collector(config.DB2)
self.s1 = SheetItf(config.GOOGLE['credentials'], config.GOOGLE['scope'], config.GOOGLE['key'], config.GOOGLE['sheet'])
def push(self):
self.s1.addToRow([str(time.strftime("%d/%m/%Y")), str(time.strftime("%H:%M:%S"))])
self.s1.addToRow(self.c1.collect())
self.s1.addToRow(self.c2.collect())
print("pushing:", self.s1.nextRow)
self.s1.pushRow()
def collect_stock():
collector = Collector()
try:
collector.collect_daily_data()
except:
try:
collector.collect_daily_data()
except:
collector.collect_daily_data()
def runTest(self):
collector = Collector(self.tmpCacheDir,
serverHost='127.0.0.1',
serverPort='8000',
serverProtocol='http',
serverUser=testAuthCreds[0],
serverPass=testAuthCreds[1],
clientId='test-fuzzer1')
config = ProgramConfiguration("mozilla-central", "x86-64", "linux", version="ba0bc4f26681")
crashInfo = CrashInfo.fromRawCrashData([], asanTraceCrash.splitlines(), config)
# TODO: This is only a rudimentary check to see if we submitted *something*.
# We should check more precisely that the information submitted is correct.
issueCount = self.getRemoteCrashEntryCount()
collector.submit(crashInfo, exampleTestCase)
self.assertEqual(self.getRemoteCrashEntryCount(), issueCount + 1)
def collect_data(mode):
video = videoCapture()
collector = Collector(mode)
directory = collector.getDirectory()
frame = video.getMainFrame()
ROI_NAME = 'Region of Interest'
print(color("Data collecting started...", Colors.yellow))
print(color("video windows is opening ", Colors.yellow))
collector.generateFolders()
while video.isRunning():
video.update()
# Add Texts and Frames
video.addRightText(mode)
for gesture in gestures:
video.addRightText(gestures[gesture] + ': ' +
str(filesCounter(directory + str(gesture))[0]))
video.addFrame(ROI_NAME,
ROIcoordinates(frame)[0],
ROIcoordinates(frame)[1],
ROIcoordinates(frame)[2],
ROIcoordinates(frame)[3])
video.display()
interrupt = cv2.waitKey(10)
ROI = video.getFrameRegion(ROI_NAME)
collector.keyPressToImage(ROI, interrupt)
if interrupt & 0xFF == 27:
video.stop()
video.releaseCamera()
cv2.destroyAllWindows()
print(color("Data collecting finished", Colors.yellow))
def work(self, login, password, proxy=None, first=False):
if (not first):
# If we aren't first, sleep a bit and let the first one create database
time.sleep(1)
print("Spawning thread {}, proxy {}".format(login, proxy))
# Move here to cope with psycopg restriction on being unable to use same session in different processes
from Collector import Collector
collector = Collector(login, password, proxy)
worker = VkWorker(collector)
def runTest(self):
collector = Collector(self.tmpCacheDir,
serverHost='127.0.0.1',
serverPort='8000',
serverProtocol='http',
serverUser=testAuthCreds[0],
serverPass=testAuthCreds[1],
clientId='test-fuzzer1')
collector.refresh()
receivedSignatures = False
for sigFile in os.listdir(self.tmpCacheDir):
receivedSignatures = True
CrashSignature.fromFile(os.path.join(self.tmpCacheDir, sigFile))
if not receivedSignatures:
self.skipTest("Server did not provide signatures")
def runTest(self):
collector = Collector(self.tmpCacheDir,
serverHost='127.0.0.1',
serverPort='8000',
serverProtocol='http',
serverUser=testAuthCreds[0],
serverPass=testAuthCreds[1],
clientId='test-fuzzer1')
collector.refresh()
receivedSignatures = False
for sigFile in os.listdir(self.tmpCacheDir):
receivedSignatures = True
CrashSignature.fromFile(os.path.join(self.tmpCacheDir, sigFile))
if not receivedSignatures:
self.skipTest("Server did not provide signatures")
def __init__(self, startTime = 0, *args, **kwargs):
super().__init__(*args, **kwargs)
self.sharedObjects = {}
self.startTime = startTime
self.xcontext = XValueContext(lambda: self.now() + self.startTime)
self.t = self.xcontext.t
self.collector = Collector()
self.tcounter = 0
self.initialize()
self.xvalues = {}
self.logging = True
def __init__(self, binsAmount):
self.gridWidth = 20
self.gridHeight = 9
self.fieldSize = 64
self.window = Tk()
self.canvas = Canvas(self.window,
width=self.fieldSize * self.gridWidth,
height=self.fieldSize * self.gridHeight)
self.binsAmount = binsAmount
self.window.title("Simulation")
self.collector = Collector(1, 1, 1)
self.positionsToVisit = []
self.mapElements = []
self.addDumps()
self.addRoads()
self.addBins()
self.addGrass()
self.MovementLogic = MovementLogic(self.mapElements, self.gridWidth,
self.gridHeight)
self.classifier = GarbageClassifier("learningExamples.txt")
def run(self):
self._collector_ready = False
self._res_list = []
collector_ref = Collector.start(self._num_executors, self)
for idx in range(self._num_executors):
self._executors[idx].tell({'type': 'run', 'collector': collector_ref})
while not self._collector_ready:
time.sleep(0.1)
collector_ref.stop()
return self._res_list
def runTest(self):
collector = Collector(self.tmpCacheDir,
serverHost='127.0.0.1',
serverPort='8000',
serverProtocol='http',
serverUser=testAuthCreds[0],
serverPass=testAuthCreds[1],
clientId='test-fuzzer1')
config = ProgramConfiguration("mozilla-central",
"x86-64",
"linux",
version="ba0bc4f26681")
crashInfo = CrashInfo.fromRawCrashData([], asanTraceCrash.splitlines(),
config)
# TODO: This is only a rudimentary check to see if we submitted *something*.
# We should check more precisely that the information submitted is correct.
issueCount = self.getRemoteCrashEntryCount()
collector.submit(crashInfo, exampleTestCase)
self.assertEqual(self.getRemoteCrashEntryCount(), issueCount + 1)
def collect_data(outfilename, measurement_time=default_time) :
collector = Collector(port,isTCP=isTCP);
time.sleep(1);
outfile_encoder = open(outfilename+'_encoder.dat','w');
outfile_encoder.write('#TIME ERROR DIRECTION TIMERCOUNT REFERENCE\n');
outfile_encoder.flush()
outfile_irig = open(outfilename+'_irig.dat','w');
outfile_irig.write('#TIMERCOUNT YEAR DAY HOUR MINUTE SECOND\n');
outfile_irig.flush()
outfile_timeout = open(outfilename+'_timeout.dat','w');
outfile_timeout.write('#TIME TYPE\n');
outfile_timeout.flush()
outfile_error = open(outfilename+'_error.dat','w');
outfile_error.write('#TIME ERRORCODE\n');
outfile_error.flush()
encoder_header = 0x1EAF;
irig_header = 0xCAFE;
timeout_header = 0x1234;
error_header = 0xE12A;
encoder_extractor = EncoderExtractor(encoder_header);
encoder_bytesize = encoder_extractor.pi.total_bytesize;
irig_extractor = IrigExtractor(irig_header);
irig_bytesize = irig_extractor.pi.total_bytesize;
timeout_extractor = TimeoutExtractor(timeout_header);
timeout_bytesize = timeout_extractor.pi.total_bytesize;
error_extractor = ErrorExtractor(error_header);
error_bytesize = error_extractor.pi.total_bytesize;
header_unpack_str = encoder_extractor.pi.header_str;
header_size = encoder_extractor.pi.header_num;
header_bytesize = encoder_extractor.pi.header_bytesize;
start_time = time.time()
while True :
encoder_frames = [];
irig_frames = [];
timeout_frames = [];
error_frames = [];
# Empty the queue and parse its contents appropriately
approx_size = collector.queue.qsize()
if approx_size>0 and verbose>0 : print('approximate size = {}'.format(approx_size));
for i in range(approx_size):
# Block=True : Block execution until there is something in the queue to retrieve
# timeout=None : the get() command will try indefinitely
data = collector.queue.get(block=True, timeout=None);
# Once data is extracted from the queue, parse its contents
# and loop until data is empty
data_len = len(data);
if verbose>0 :
print('obtained data size = {}'.format(data_len));
pass;
parse_index = 0;
while parse_index < data_len:
if verbose>0 : print('parse_index = {} / data_len = {}'.format(parse_index, data_len));
# Extract header
header = data[parse_index : parse_index + header_bytesize];
if verbose>0 :
if header!=0 : print('obtained header (size) = {} ({})'.format(header,len(header)));
elif verbose>1 : print('obtained header (size) = {} ({})'.format(header,len(header)));
pass;
# unpack from binary ( byte order: little endian(<), format : L (unsigned long) )
header = struct.unpack(("%s%s" % (endian, header_unpack_str)), header)[0]
# Check for Encoder packet
if header == encoder_header:
if verbose>0 : print(' header == encoder');
encoder_frames.append(encoder_extractor.extract(data, parse_index));
parse_index += encoder_bytesize
elif header == irig_header:
if verbose>0 : print(' header == irig');
irig_frames.append(irig_extractor.extract(data, parse_index));
parse_index += irig_bytesize
elif header == timeout_header:
if verbose>0 : print(' header == timeout');
timeout_frames.append(timeout_extractor.extract(data, parse_index));
parse_index += timeout_bytesize
elif header == error_header:
if verbose>0 : print(' header == error');
error_frames.append(error_extractor.extract(data, parse_index));
parse_index += error_bytesize
elif header == 0:
if verbose>1 : print(' header == 0');
parse_index += header_bytesize
#break;
else:
try :
raise RuntimeError(("Bad header! This is not encoder/irig/timeout/error header! : %s" % (str(header))))
except RuntimeError as e:
print(e);
if verbose>0 :
print('###get data###');
print(data);
print('##############');
pass;
#sys.exit(-1);
break;
pass;
pass;
pass; # end of ``while parse_index < data_len:``
# Reset data string
data = ''
pass; # end of loop over ``i``
currenttime = (int)(time.time());
# write encoder data
for frame in encoder_frames :
ncount = len(frame['timercount']);
for i in range(ncount) :
outfile_encoder.write('{} {} {} {} {}\n'.format(currenttime, 1-frame['error'][i],frame['quad'][i],frame['timercount'][i],frame['position'][i]));
pass;
pass;
# write irig data
for frame in irig_frames :
outfile_irig.write('{} {} {} {} {} {}\n'.format(frame['timercount'],frame['year'],frame['day'],frame['hour'],frame['minute'],frame['second']));
pass;
# write timeout data
for frame in timeout_frames :
outfile_timeout.write('{} {}\n'.format(currenttime, frame['type']));
pass;
# write error data
for frame in error_frames :
outfile_error.write('{} {}\n'.format(currenttime, frame['error']));
pass;
# flush output
outfile_encoder.flush();
outfile_irig.flush();
outfile_timeout.flush();
outfile_error.flush();
stop_time = time.time()
if(stop_time - start_time >= measurement_time):
print(f'{measurement_time} sec have passed and stopped UDP')
break
pass; # end of ``while True :``
collector.stop();
outfile_encoder.close();
outfile_irig.close();
outfile_timeout.close();
outfile_error.close();
return 0;
def doExperiment(self, index):
collector = Collector()
path = os.getcwd()
trainPath = os.path.join(path, 'train')
testPath = os.path.join(path, 'test')
if index == 2:
print('you choose stopWord experiment')
print('please wait...')
name = 'stopWord.txt'
fileName = os.path.join(path, name)
f = open(fileName, 'r', encoding='gb18030')
line = f.read()
f.close()
collector.stopWord += line.split('\n')
resultPath = os.path.join(path, 'stopWord')
modelName = 'demo-model-exp2.txt'
resultName = 'demo-result-exp2.txt'
self.doExpClassify(trainPath, testPath, resultPath, collector,
modelName, resultName)
elif index == 3:
print('you choose word length experiment')
print('please wait...')
collector.removeWordLength[0] = 2
collector.removeWordLength[1] = 9
resultPath = os.path.join(path, 'wordLength')
modelName = 'demo-model-exp3.txt'
resultName = 'demo-result-exp3.txt'
self.doExpClassify(trainPath, testPath, resultPath, collector,
modelName, resultName)
elif index == 4:
print('you choose infrequent experiment')
choose = input(
'please choose value(0) or percentage(1),default is value')
if choose == '1':
i = input(
'please enter the percentage(%)(5%-25%),default is 5%: ')
i = int(i)
if i < 5 or i > 25:
i = 0.05
else:
i = float(i / 100)
else:
i = input('please enter the value(1-20), defalut is 1: ')
i = int(i)
if i < 1 or i > 20:
i = 1
print('please wait...')
resultPath = os.path.join(path, 'infrequent')
curCollector = Collector()
modelName = 'demo-model-exp4.txt'
resultName = 'demo-result-exp4.txt'
curCollector.dataCollector(trainPath)
curCollector.infrequentProcess(i)
curCollector.buildModel(resultPath, modelName)
curCollector.doClassify(testPath, resultPath, resultName)
elif index == 5:
print('you choose delta change experiment')
i = input(
'please choose the value of delta(0.1-1), default is 0.5: ')
print('please wait...')
if float(i) < 0.1 or float(i) > 1:
i = 0.5
newDelta = float(i)
resultPath = os.path.join(path, 'delta')
curCollector = Collector()
curCollector.delta = newDelta
modelName = 'demo-model-exp5.txt'
resultName = 'demo-result-exp5.txt'
self.doExpClassify(trainPath, testPath, resultPath, curCollector,
modelName, resultName)
else:
print('you choose baseLine experiment')
print('please wait...')
resultPath = os.path.join(path, 'baseLine')
modelName = 'demo-model-base.txt'
resultName = 'demo-result-base.txt'
self.doExpClassify(trainPath, testPath, resultPath, collector,
modelName, resultName)
# Each score represents level of confidence for each of the objects.
detection_scores = inference.get_model_detection_scores()
detection_classes = inference.get_model_detection_classes()
# Number of objects detected.
num_detections = inference.get_model_detected_objects()
camera = PiCamera()
camera.resolution = (IM_WIDTH, IM_HEIGHT)
camera.framerate = 10
rawCapture = PiRGBArray(camera, size=(IM_WIDTH, IM_HEIGHT))
rawCapture.truncate(0)
# Initialize frame rate calculation
collector = Collector()
frame_rate_calc = collector.frame_rate_calc
freq = collector.freq
font = collector.font
# Initialize publisher service.
publisher = Publisher()
for frame1 in camera.capture_continuous(rawCapture,
format="bgr",
use_video_port=True):
t1 = cv2.getTickCount()
# Acquire frame and expand frame dimensions to have shape:
# [1, None, None, 3]
from datetime import datetime
df = pd.read_csv("dataset.csv")
temp = open("precips.txt")
skip = len(temp.readlines())
print(skip)
for idx, row in enumerate(df.iterrows()):
if idx < skip:
continue
# if idx == 0:
# break
row = row[1]
lat = str(row.BEGIN_LAT)
lon = str(row.BEGIN_LON)
date = row.BEGIN_DATE
parts = date.split("/")
date = parts[0]+"/" + parts[2] + "/" + parts[1]
c = Collector(lat, lon, date)
file = open("precips.txt", "a")
data = c.getData()
if data[0] == 'no' or data[1] == 'no':
print("API RAN OUT: . Didn't get", idx)
break
if idx % 50 == 0:
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(idx, current_time, " || ", date, lat, lon, data)
file.write(str(row.EVENT_ID) + ";;" + data[0] + ";;" + data[1] + "|||\n")
# file.close()
def __init__ (self, config):
self.c1 = Collector(config.DB)
self.c2 = Collector(config.DB2)
self.s1 = SheetItf(config.GOOGLE['credentials'], config.GOOGLE['scope'], config.GOOGLE['key'], config.GOOGLE['sheet'])
# Get utils module
import utils
# Setup environment variables
import Process
process = Process.getInstance(open('trading-data/.env', 'r'))
program = sys.argv[1]
if process.env['MASTER_SWITCH'] == 'on':
if program == 'collect' and len(sys.argv) > 2:
target_date = utils.parseISODate(sys.argv[2])
# kick off data collection sequence
from Collector import Collector
collector = Collector(target_date, False)
collector.start()
elif program == 'collect' and len(sys.argv) <= 2:
from Collector import Collector
collector = Collector(datetime.now(), True)
collector.start()
elif program == 'train':
from Learner import Learner
learner = Learner(False)
learner.start()
elif program == 'predict':
from Learner import Learner
learner = Learner(True, False)
learner.start()
else:
print 'Invalid parameters given. Shutting down...'
import configparser
import os
from Database.DBManager import DBManager
from Collector import Collector
SOURCE_PATH = os.path.dirname(os.path.abspath(__file__))
CONFIG_FILE_NAME = SOURCE_PATH + "/settings.env"
print(CONFIG_FILE_NAME)
# Starting point
config = configparser.ConfigParser()
config.read(CONFIG_FILE_NAME)
config_raw = configparser.RawConfigParser()
config_raw.read(CONFIG_FILE_NAME)
TIME_FORMAT = config_raw.get("Common", "TIME_FORMAT")
DB_HOST = config.get("Database", "DB_HOST")
DB_DATABASE = config.get("Database", "DB_DATABASE")
DB_USERNAME = config.get("Database", "DB_USERNAME")
DB_PASSWORD = config.get("Database", "DB_PASSWORD")
db_manager = DBManager(DB_HOST, DB_USERNAME, DB_PASSWORD, DB_DATABASE)
Collector.collect(db_manager, TIME_FORMAT)
class dbBuilder:
def __init__(self, species, download=False, withEns=True):
self.species = species
self.dbName = None
self.data = Collector(self.species)
self.data.collectAll(download=download, withEns=withEns)
self.TranscriptNoProteinRec = {}
self.DomainsSourceDB = 'DB_merged.sqlite'
self.DomainOrg = DomainOrganizer(download=download)
def create_tables_db(self, merged=True, dbName=None):
"""
Create a transcripts table in the specie database and fills with ucsc transcripts data
"""
if dbName is not None:
self.dbName = dbName
elif merged:
self.dbName = 'DB_merged'
else:
self.dbName = 'DB_' + self.species
print("Creating database: {}...".format(self.dbName))
with connect(self.dbName + '.sqlite') as con:
cur = con.cursor()
cur.executescript('DROP TABLE IF EXISTS Genes;')
print('Creating the table: Genes')
cur.execute('''
CREATE TABLE Genes(
gene_GeneID_id TEXT UNIQUE,
gene_ensembl_id TEXT UNIQUE,
gene_symbol TEXT,
synonyms TEXT,
chromosome TEXT,
strand TEXT,
specie TEXT,
PRIMARY KEY(gene_GeneID_id, gene_ensembl_id, gene_symbol)
);'''
)
cur.executescript("DROP TABLE IF EXISTS transcripts;")
print('Creating the table: Transcripts')
cur.execute('''
CREATE TABLE Transcripts(
transcript_refseq_id TEXT UNIQUE,
transcript_ensembl_id TEXT UNIQUE,
tx_start INTEGER,
tx_end INTEGER,
cds_start INTEGER,
cds_end INTEGER,
exon_count INTEGER,
gene_GeneID_id TEXT,
gene_ensembl_id TEXT,
protein_refseq_id TEXT,
protein_ensembl_id TEXT,
PRIMARY KEY (transcript_refseq_id, transcript_ensembl_id),
FOREIGN KEY(gene_GeneID_id, gene_ensembl_id) REFERENCES Genes(gene_GeneID_id, gene_ensembl_id),
FOREIGN KEY(protein_refseq_id,protein_ensembl_id) REFERENCES Proteins(protein_refseq_id,protein_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS Exons;")
print('Creating the table: Exons')
cur.execute('''
CREATE TABLE Exons(
gene_GeneID_id TEXT,
gene_ensembl_id TEXT,
genomic_start_tx INTEGER,
genomic_end_tx INTEGER,
PRIMARY KEY (gene_GeneID_id, gene_ensembl_id, genomic_start_tx, genomic_end_tx),
FOREIGN KEY(gene_GeneID_id, gene_ensembl_id) REFERENCES Genes(gene_GeneID_id, gene_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS Transcript_Exon;")
print('Creating the table: Transcript_Exon')
cur.execute('''
CREATE TABLE Transcript_Exon(
transcript_refseq_id TEXT,
transcript_ensembl_id TEXT,
order_in_transcript INTEGER,
genomic_start_tx INTEGER,
genomic_end_tx INTEGER,
abs_start_CDS INTEGER,
abs_end_CDS INTEGER,
PRIMARY KEY(transcript_refseq_id, transcript_ensembl_id, order_in_transcript),
FOREIGN KEY(transcript_refseq_id, transcript_ensembl_id)
REFERENCES Transcripts(transcript_refseq_id, transcript_ensembl_id),
FOREIGN KEY(genomic_start_tx, genomic_end_tx)\
REFERENCES Exons(genomic_start_tx, genomic_end_tx)
);'''
)
cur.executescript("DROP TABLE IF EXISTS Proteins;")
print('Creating the table: Proteins')
cur.execute('''
CREATE TABLE Proteins(
protein_refseq_id TEXT UNIQUE,
protein_ensembl_id TEXT UNIQUE,
description TEXT,
synonyms TEXT,
length INTEGER,
transcript_refseq_id TEXT,
transcript_ensembl_id TEXT,
PRIMARY KEY(protein_refseq_id, protein_ensembl_id),
FOREIGN KEY(transcript_refseq_id, transcript_ensembl_id) REFERENCES Transcripts(transcript_refseq_id, transcript_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS DomainType;")
print('Creating the table: DomainType')
cur.execute('''
CREATE TABLE DomainType(
type_id INTEGER NOT NULL PRIMARY KEY UNIQUE,
name TEXT,
other_name TEXT,
description TEXT,
CDD_id TEXT,
cdd TEXT,
pfam TEXT,
smart TEXT,
tigr TEXT,
interpro TEXT
);'''
)
cur.executescript("DROP TABLE IF EXISTS DomainEvent;")
print('Creating the table: DomainEvent')
cur.execute('''
CREATE TABLE DomainEvent(
protein_refseq_id TEXT,
protein_ensembl_id TEXT,
type_id INTEGER,
AA_start INTEGER,
AA_end INTEGER,
nuc_start INTEGER,
nuc_end INTEGER,
total_length INTEGER,
ext_id TEXT,
splice_junction BOOLEAN,
complete_exon BOOLEAN,
PRIMARY KEY(protein_refseq_id, protein_ensembl_id, type_id, AA_start, total_length),
FOREIGN KEY(type_id) REFERENCES DomainType(type_id),
FOREIGN KEY(protein_refseq_id, protein_ensembl_id)
REFERENCES Proteins(protein_refseq_id, protein_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS SpliceInDomains;")
print('Creating the table: SpliceInDomains')
cur.execute("""
CREATE TABLE SpliceInDomains(
transcript_refseq_id TEXT,
transcript_ensembl_id TEXT,
exon_order_in_transcript INTEGER,
type_id INTEGER,
total_length INTEGER,
domain_nuc_start INTEGER,
included_len INTEGER,
exon_num_in_domain INTEGER,
PRIMARY KEY (transcript_refseq_id, transcript_ensembl_id, exon_order_in_transcript, type_id,\
total_length, domain_nuc_start),
FOREIGN KEY(transcript_refseq_id, transcript_ensembl_id)
REFERENCES Transcripts(transcript_refseq_id, transcript_ensembl_id),
FOREIGN KEY(exon_order_in_transcript) REFERENCES Transcript_Exon(order_in_transcript),
FOREIGN KEY(type_id) REFERENCES DomainType(type_id),
FOREIGN KEY(domain_nuc_start, total_length) REFERENCES DomainEvent(Nuc_start, total_length)
);"""
)
if merged:
cur.executescript("DROP TABLE IF EXISTS Orthology;")
print('Creating the table: Orthology')
cur.execute("""
CREATE TABLE Orthology(
A_ensembl_id TEXT,
A_GeneSymb TEXT,
A_species TEXT,
B_ensembl_id TEXT,
B_GeneSymb TEXT,
B_species TEXT,
PRIMARY KEY (A_ensembl_id, B_ensembl_id),
FOREIGN KEY (A_ensembl_id, B_ensembl_id, A_GeneSymb, B_GeneSymb, A_species, B_species)
REFERENCES Genes(gene_ensembl_id, gene_ensembl_id, gene_symbol, gene_symbol, specie, specie)
);"""
)
# ~~~ disconnect database ~~~
def create_index(self):
""" Creates index for for efficient searches"""
with connect(self.dbName + '.sqlite') as con:
cur = con.cursor()
cur.execute('''CREATE INDEX geneTableIndexBySpecies ON Genes(specie);''')
cur.execute('''CREATE INDEX transcriptTableIndexByGene ON Transcripts(gene_GeneID_id) ;''')
cur.execute(
'''CREATE INDEX exonsInTranscriptsTableIndexByTranscripts ON Transcript_Exon(transcript_refseq_id) ;''')
cur.execute('''CREATE INDEX domainEventsTableIndexByProtein ON DomainEvent(protein_refseq_id) ;''')
cur.execute('''CREATE INDEX domainEventsTableIndexByEnsembl ON DomainEvent(protein_ensembl_id);''')
cur.execute(
'''CREATE INDEX exonInTranscriptsTableIndexByEnsembl ON Transcript_Exon(transcript_ensembl_id);''')
def fill_in_db(self, CollectDomainsFromMerged=True, merged=True, dbName=None):
"""
This is filling the database with the collected data for a single species.
if used db is "merged" than set True to the param. if False than a species unique db will be created.
"""
if dbName is not None:
self.dbName = dbName
elif merged:
self.dbName = 'DB_merged'
else:
self.dbName = 'DB_' + self.species
if CollectDomainsFromMerged: # to keep domain ids consistent between the merged & single species db
self.DomainOrg.collectDatafromDB(self.DomainsSourceDB)
preDomains = set(self.DomainOrg.allDomains.keys())
with connect(self.dbName + '.sqlite') as con:
print("Connected to " + self.dbName + "...")
print("Filling in the tables...")
cur = con.cursor()
geneSet = set()
uExon = set()
relevantDomains = set()
for tID, transcript in self.data.Transcripts.items():
ensemblkey = False
if tID.startswith("ENS"):
ensemblkey = True
e_counts = len(transcript.exon_starts)
# insert into Transcripts table
if transcript.CDS is None:
print("Transcript {} from {} has None in CDS".format(tID, self.species))
transcript.CDS = transcript.tx
values = (transcript.refseq, transcript.ensembl,) + transcript.tx + transcript.CDS + \
(e_counts, transcript.gene_GeneID, transcript.gene_ensembl,
transcript.protein_refseq, transcript.protein_ensembl,)
cur.execute('''INSERT INTO Transcripts
(transcript_refseq_id, transcript_ensembl_id, tx_start, tx_end, cds_start,\
cds_end, exon_count, gene_GeneID_id, gene_ensembl_id, protein_refseq_id, protein_ensembl_id)
VALUES(?,?,?,?,?,?,?,?,?,?,?)''', values)
# insert into Genes table
if transcript.gene_GeneID not in geneSet and \
transcript.gene_ensembl not in geneSet:
gene = self.data.Genes.get(
transcript.gene_GeneID if transcript.gene_GeneID is not None else transcript.gene_ensembl,
self.data.Genes.get(transcript.gene_ensembl, None))
if gene is None:
raise ValueError("No gene in Genes for transcript {}, {}. GeneID: {}, ensembl gene: {}".format(
transcript.refseq, transcript.ensembl, transcript.gene_GeneID, transcript.gene_ensembl))
# if ensemblkey:
# gene = self.data.Genes.get(transcript.gene_ensembl, self.data.Genes[transcript.gene_GeneID])
# # syno = gene.synonyms
# else:
# gene = self.data.Genes[transcript.gene_GeneID]
# # syno = [self.data.Genes[transcript.gene_GeneID].synonyms
# # if transcript.gene_GeneID is not None else None][0]
values = (gene.GeneID, gene.ensembl, gene.symbol,
gene.synonyms, gene.chromosome, gene.strand, self.species,)
cur.execute(''' INSERT INTO Genes
(gene_GeneID_id, gene_ensembl_id, gene_symbol, synonyms, chromosome,\
strand, specie)
VALUES (?, ?, ?, ?, ?, ?, ?)''', values)
geneSet.add(gene.GeneID)
geneSet.add(gene.ensembl)
geneSet = geneSet - {None}
start_abs, stop_abs = transcript.exons2abs()
ex_num = 0
starts = transcript.exon_starts.copy()
ends = transcript.exon_ends.copy()
for iEx in range(e_counts):
ex_num += 1
# insert into Transcript_Exon table
values = (transcript.refseq, transcript.ensembl, ex_num, starts[iEx], ends[iEx],
start_abs[iEx], stop_abs[iEx],)
cur.execute(''' INSERT INTO Transcript_Exon
(transcript_refseq_id, transcript_ensembl_id, order_in_transcript,\
genomic_start_tx, genomic_end_tx, abs_start_CDS, abs_end_CDS)
VALUES (?, ?, ?, ?, ?, ?, ?)''', values)
# insert into Exons table
values = (transcript.gene_GeneID, transcript.gene_ensembl, starts[iEx], ends[iEx],)
if values not in uExon:
uExon.add(values)
cur.execute('''INSERT INTO Exons
(gene_GeneID_id, gene_ensembl_id, genomic_start_tx, genomic_end_tx)
VALUES (?, ?, ?, ?)''', values)
# insert into Proteins table
if ensemblkey:
protID = transcript.protein_ensembl
else:
protID = transcript.protein_refseq
protein = self.data.Proteins[protID]
values = (protein.refseq, protein.ensembl, protein.description, protein.length,
protein.synonyms, transcript.refseq, transcript.ensembl,)
cur.execute(''' INSERT INTO Proteins
(protein_refseq_id, protein_ensembl_id, description, length, synonyms, transcript_refseq_id, transcript_ensembl_id)
VALUES (?, ?, ?, ?, ?, ?, ?)''', values)
splicin = set()
# domeve = set()
Domdf = pd.DataFrame(columns=["protein_refseq_id", "protein_ensembl_id", "type_id",
"AA_start", "AA_end", "nuc_start", "nuc_end", "total_length",
"ext_id", "splice_junction", "complete_exon"])
for reg in self.data.Domains.get(protID, [None]):
if reg is None:
continue
regID = self.DomainOrg.addDomain(reg)
if regID is None:
continue
relevantDomains.add(regID)
relation, exon_list, length = reg.domain_exon_relationship(start_abs, stop_abs)
total_length = reg.nucEnd - reg.nucStart + 1 # adding one because coordinates are full-closed!
splice_junction = 0
complete = 0
if relation == 'splice_junction':
splice_junction = 1
for i in range(len(exon_list)):
values = (transcript.refseq, transcript.ensembl,
exon_list[i], reg.nucStart, regID,
total_length, length[i], i + 1,)
if values not in splicin:
cur.execute(''' INSERT INTO SpliceInDomains
(transcript_refseq_id, transcript_ensembl_id,\
exon_order_in_transcript, domain_nuc_start, type_id,\
total_length, included_len, exon_num_in_domain)
VALUES (?, ?, ?, ?, ?, ?, ?, ?)''', values)
splicin.add(values)
elif relation == 'complete_exon':
complete = 1
# insert into domain event table
ldf = Domdf.shape[0]
extWithInter = "; ".join([reg.extID, self.DomainOrg.allDomains[regID][-1]]) if \
self.DomainOrg.allDomains[regID][-1] is not None else reg.extID
values = (protein.refseq, protein.ensembl, regID,
reg.aaStart, reg.aaEnd, reg.nucStart, reg.nucEnd, total_length,
extWithInter, splice_junction, complete,)
Domdf.loc[ldf] = list(values)
Domdf = Domdf.drop_duplicates()
Domdf = Domdf.fillna(-1)
Domdf = Domdf.groupby(["protein_refseq_id", "protein_ensembl_id", "type_id",
"AA_start", "AA_end", "nuc_start", "nuc_end", "total_length",
"splice_junction", "complete_exon"],
as_index=False, sort=False).agg(
lambda col: "; ".join(set(col))) # groupby all besides ext_ID
Domdf = Domdf.replace(-1, np.nan)
Domdf.to_sql("DomainEvent", con, if_exists="append", index=False)
# ~~~ end of loop iterating over transcripts ~~~
bp = time.time()
if merged:
relevantDomains = preDomains.union(relevantDomains)
print('Recreating the table: DomainType and update domains')
cur.executescript("DROP TABLE IF EXISTS DomainType;")
print('Creating the table: DomainType')
cur.execute('''
CREATE TABLE DomainType(
type_id INTEGER NOT NULL PRIMARY KEY UNIQUE,
name TEXT,
other_name TEXT,
description TEXT,
CDD_id TEXT,
cdd TEXT,
pfam TEXT,
smart TEXT,
tigr TEXT,
interpro TEXT
);'''
)
# insert into domain type table
postDomains = set(self.DomainOrg.allDomains.keys())
print("from all {} domains in organizer, {} not in relevant domains".format(len(postDomains),
len(postDomains.difference(relevantDomains))))
for typeID in relevantDomains:
if typeID in self.DomainOrg.allDomains.keys():
values = (typeID,) + self.DomainOrg.allDomains[typeID]
cur.execute(''' INSERT INTO DomainType
(type_id, name, other_name, description, CDD_id, cdd,\
pfam, smart, tigr, interpro)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', values)
print("#### Filling in domain type table: %s seconds" % (time.time() - bp))
con.commit()
# ~~~ disconnect database ~~~
def AddOrthology(self, orthologsDict):
"""
This function adds the orthology data to the database, only for the genes included in the database.
Changes the database with no returned output.
@param orthologsDict: created by OrthologsBuilder module, called by the main script.
@return: None
"""
MainOrtho = pd.DataFrame(columns=['A_ensembl_id', 'A_GeneSymb', 'A_species',
'B_ensembl_id', 'B_GeneSymb', 'B_species'])
db_data = dict()
orthology_species = set([spec for x in orthologsDict.keys() for spec in x])
with connect(self.dbName + '.sqlite') as con:
cur = con.cursor()
schema = cur.execute("PRAGMA table_info('Orthology')").fetchall()
for spec in orthology_species:
db_data[spec] = pd.read_sql(
"SELECT gene_ensembl_id,gene_symbol,specie FROM Genes WHERE specie='{}'".format(spec),
con)
print("collecting orthology data for:")
for couple, ortho in orthologsDict.items():
print("\t{} and {}".format(couple[0], couple[1]))
merged_df = None
n = 0
for spec in couple:
db_data[spec]['gene_symbol'] = db_data[spec]['gene_symbol'].str.upper()
db_data[spec].columns = db_data[spec].columns.str.replace('gene_ensembl_id', spec + "_ID")
if n == 0:
merged_df = pd.merge(db_data[spec], ortho)
else:
merged_df = pd.merge(db_data[spec], merged_df)
label = 'A' if n == 0 else 'B'
merged_df.columns = merged_df.columns.str.replace("specie", label + "_Species")
merged_df.columns = merged_df.columns.str.replace("gene_symbol", label + "_GeneSymb")
merged_df.columns = merged_df.columns.str.replace(spec + "_ID", label + "_ensembl_id")
merged_df = merged_df.drop(spec + "_name", axis=1)
n += 1
MainOrtho = MainOrtho.append(merged_df, sort=False)
MainOrtho = MainOrtho.drop_duplicates()
MainOrtho = MainOrtho.groupby(["A_ensembl_id", "B_ensembl_id"], as_index=False, sort=False).agg(
lambda col: ', '.join(set(col)))
print("Filling in Orthology table...")
try:
MainOrtho.to_sql("Orthology", con, if_exists="replace", schema=schema, index=False)
except Exception as err:
print(err)
MainOrtho.to_csv("OrthologyTable.Failed.csv")
print("Filling Orthology table complete!")
# Configure logging
app_logger = logging.getLogger(__name__)
app_logger.setLevel(logging.INFO)
es_logger = logging.getLogger('elasticsearch.trace')
es_logger.setLevel(logging.WARNING)
handler = logging.FileHandler(filename='burner_%s.log' %
datetime.datetime.now().strftime('%Y%m%d'),
mode='a')
handler.setLevel(logging.INFO)
handler.setFormatter(
logging.Formatter("%(asctime)s %(levelname)s %(message)s",
"%Y-%m-%d %H:%M"))
app_logger.addHandler(handler)
es_logger.addHandler(handler)
app_logger.info('Started')
# Read configuration file
config = ConfigParser.RawConfigParser()
config.read('config.cfg')
collector = Collector.Collector(config, app_logger)
updater = Updater(config, app_logger)
updater.store_users_expenses(collector.get_users_expenses())
app_logger.info('Completed\n')
class Simulation(object):
def checkIfPositionIsEmpty(self, position):
for i in self.mapElements:
if i.position == position:
return False
return True
def __init__(self, binsAmount):
self.gridWidth = 20
self.gridHeight = 9
self.fieldSize = 64
self.window = Tk()
self.canvas = Canvas(self.window,
width=self.fieldSize * self.gridWidth,
height=self.fieldSize * self.gridHeight)
self.binsAmount = binsAmount
self.window.title("Simulation")
self.collector = Collector(1, 1, 1)
self.positionsToVisit = []
self.mapElements = []
self.addDumps()
self.addRoads()
self.addBins()
self.addGrass()
self.MovementLogic = MovementLogic(self.mapElements, self.gridWidth,
self.gridHeight)
self.classifier = GarbageClassifier("learningExamples.txt")
def addDumps(self):
types = ['plastic', 'paper', 'glass', 'other']
n = 0
for j in types:
new = Dump(n, 0, j)
n = n + 1
self.mapElements.append(new)
def addRoad(self, position1, position2):
if position1[0] == position2[0]:
for i in range(position1[1], position2[1] + 1):
if self.checkIfPositionIsEmpty([position1[0], i]):
element = Road(position1[0], i)
self.mapElements.append(element)
elif position1[1] == position2[1]:
for i in range(position1[0], position2[0] + 1):
if self.checkIfPositionIsEmpty([i, position1[1]]):
element = Road(i, position1[1])
self.mapElements.append(element)
def addRoads(self):
self.addRoad([0, 1], [self.gridWidth, 1])
self.addRoad([0, 4], [self.gridWidth, 4])
self.addRoad([0, 7], [self.gridWidth, 7])
r = randint(1, 6)
for i in range(0, r):
s = randint(1, self.gridWidth - 2)
self.addRoad([s, 1], [s, self.gridHeight - 2])
def addBins(self):
for i in range(0, self.binsAmount):
rightPosition = False
while not rightPosition:
x = randint(0, self.gridWidth - 1)
y = randint(0, self.gridHeight - 1)
if self.checkIfPositionIsEmpty([x, y]):
rightPosition = True
element = Bin(x, y)
self.positionsToVisit.append([x, y])
self.mapElements.append(element)
def addGrass(self):
for i in range(0, self.gridWidth):
for j in range(0, self.gridHeight):
if self.checkIfPositionIsEmpty([i, j]):
element = Grass(i, j)
self.mapElements.append(element)
def display(self):
for i in self.mapElements:
x = i.position[0]
y = i.position[1]
self.canvas.create_image(x * self.fieldSize,
y * self.fieldSize,
image=i.image,
anchor=NW)
x = self.collector.state.position[0]
y = self.collector.state.position[1]
self.canvas.create_image(x * self.fieldSize,
y * self.fieldSize,
image=self.collector.image,
anchor=NW)
self.canvas.pack()
def update(self):
self.display()
self.window.update_idletasks()
self.window.update()
time.sleep(0.5)
def classify(self):
for i in range(0, 5):
r = randint(1, 40)
name = "./photos/test/test" + str(r) + ".jpg"
im = ImageExample(name)
image = ImageTk.PhotoImage(Image.open(name))
result = self.classifier.test(im.getString())
self.canvas.create_image(350, 100, image=image, anchor=NW)
self.canvas.pack()
self.window.update_idletasks()
self.window.update()
time.sleep(0.5)
self.canvas.create_text(420,
150,
fill="black",
font="Times 20",
text=result)
self.canvas.pack()
self.window.update_idletasks()
self.window.update()
time.sleep(2)
def predictDigits(self):
sess = tf.Session()
saver = tf.train.import_meta_graph('./src/model/my-model.meta')
saver.restore(sess, tf.train.latest_checkpoint('./model'))
print("Model został wczytany.")
graph = tf.get_default_graph()
output_layer = graph.get_tensor_by_name("output:0")
X = graph.get_tensor_by_name("X:0")
r = randint(0, 9)
img = np.invert(
Image.open("../test_digits/house_test_" + str(r) + ".png"))
prediction = sess.run(tf.argmax(output_layer, 1), feed_dict={X: [img]})
print("Rozpoznanie dla testowanego obrazka:", np.squeeze(prediction))
def start(self):
for p in self.positionsToVisit:
for zz in self.mapElements:
if zz.position == p:
zz.searching = True
zz.updateImage()
self.update()
actions = self.MovementLogic.getActions(self.collector.state, p)
if actions is not None:
for i in actions:
print(i)
self.update()
self.collector.doAction(i)
self.update()
self.predictDigits()
self.classify()
for zz in self.mapElements:
if zz.position == p:
zz.searching = False
zz.updateImage()
def data():
session = boto3.Session(region_name='us-west-2')
collector = Collector(session)
headers = {'Content-Type': 'application/json'}
return (collector.collect(), 200, headers)
#thread management
from queue import Queue
from collections import deque
import time
from Collector import Collector
from Detector import Detector
from Processor import Processor
# parameters
maxqueuelen = 200
if __name__ == "__main__":
# queue for images from camera
queue_raw = Queue(maxsize=maxqueuelen)
# queue for boat candidates & frame
queue_detectors = Queue(maxsize=maxqueuelen)
collector = Collector(queue_raw)
detector = Detector(queue_raw, queue_detectors)
processor = Processor(queue_detectors)
collector.start()
detector.start()
processor.start()
while True:
print("queue_raw len: {} queue_detectors len: {}".\
format( queue_raw.qsize(), queue_detectors.qsize()))
time.sleep(60)
def run(samples,channel, use, train,short, preprocess_chain = []):
if use == "xgb":
from XGBModel import XGBObject as modelObject
parameters = "conf/parameters_xgb.json"
if use == "keras":
from KerasModel import KerasObject as modelObject
parameters = "conf/parameters_keras.json"
read = Reader(channel = channel,
config_file = samples,
folds=2)
target_names = read.config["target_names"]
variables = read.config["variables"]
if not os.path.exists("models"):
os.mkdir("models")
modelname = "models/{0}.{1}".format(channel,use)
scaler = None
if train:
print "Training new model"
print "Loading Training set"
trainSet = read.getSamplesForTraining()
print "Fit Scaler to training set...",
scaler = trainScaler(trainSet, variables )
print " done. Dumping for later."
with open("models/StandardScaler.{0}.pkl".format(channel), 'wb') as FSO:
cPickle.dump(scaler, FSO , 2)
trainSet = applyScaler(scaler, trainSet, variables)
model = modelObject( parameter_file = parameters,
variables=variables,
target_names = target_names )
model.train( trainSet )
model.save(modelname)
else:
if os.path.exists("models/StandardScaler.{0}.pkl".format(channel) ):
print "Loading Scaler"
with open( "models/StandardScaler.{0}.pkl".format(channel), "rb" ) as FSO:
scaler = cPickle.load( FSO )
print "Loading model and predicting."
model = modelObject( filename = modelname )
where = ""
coll = Collector( channel = channel,
var_name = "pred_prob",
target_names = target_names,
path = use,
recreate = True,
rebin = False )
print "Predicting simulation"
for sample, sampleName in read.get(what = "nominal"):
pred = model.predict( applyScaler(scaler, sample, variables), where )
coll.addPrediction(pred, sample, sampleName)
print "Adding looser samples to predictions"
for sample, sampleName in read.get(what = "more"):
pred = model.predict( applyScaler(scaler, sample, variables), where )
coll.addPrediction(pred, sample, sampleName)
print "Predicting data"
for sample, sampleName in read.get(what = "data"):
pred = model.predict( applyScaler(scaler, sample, variables), where )
coll.addPrediction(pred, sample, sampleName)
if not short:
print "Predicting TES shapes"
for sample, sampleName in read.get(what = "tes"):
pred = model.predict( applyScaler(scaler, sample, variables), where )
coll.addPrediction(pred, sample, sampleName)
print "Predicting JES shapes"
for sample, sampleName in read.get(what = "jec"):
pred = model.predict( applyScaler(scaler, sample, variables), where )
coll.addPrediction(pred, sample, sampleName)
coll.createDC(writeAll = True)
plot = Plotter( channel= channel,
naming = read.processes,
path = use )
plot.makePlots()
class dbBuilder:
def __init__(self, species, download=False, withEns=True):
self.species = species
self.dbName = None
self.data = Collector(self.species)
self.data.collectAll(download=download, withEns=withEns)
self.TranscriptNoProteinRec = {}
self.DomainsSourceDB = 'DB_merged.sqlite'
self.DomainOrg = DomainOrganizer()
def create_tables_db(self, merged=True, dbName=None):
"""
Create a transcripts table in the specie database and fills with ucsc transcripts data
"""
if dbName is not None:
self.dbName = dbName
elif merged:
self.dbName = 'DB_merged'
else:
self.dbName = 'DB_' + self.species
print("Creating database: {}...".format(self.dbName))
with connect(self.dbName + '.sqlite') as con:
cur = con.cursor()
cur.executescript('DROP TABLE IF EXISTS Genes;')
print('Creating the table: Genes')
cur.execute('''
CREATE TABLE Genes(
gene_GeneID_id TEXT,
gene_ensembl_id TEXT,
gene_symbol TEXT,
synonyms TEXT,
chromosome TEXT,
strand TEXT,
specie TEXT,
PRIMARY KEY(gene_GeneID_id, gene_ensembl_id, gene_symbol)
);'''
)
cur.executescript("DROP TABLE IF EXISTS transcripts;")
print('Creating the table: Transcripts')
cur.execute('''
CREATE TABLE Transcripts(
transcript_refseq_id TEXT,
transcript_ensembl_id TEXT,
tx_start INTEGER,
tx_end INTEGER,
cds_start INTEGER,
cds_end INTEGER,
exon_count INTEGER,
gene_GeneID_id TEXT,
gene_ensembl_id TEXT,
protein_refseq_id TEXT,
protein_ensembl_id TEXT,
PRIMARY KEY (transcript_refseq_id, transcript_ensembl_id),
FOREIGN KEY(gene_GeneID_id, gene_ensembl_id) REFERENCES Genes(gene_GeneID_id, gene_ensembl_id),
FOREIGN KEY(protein_refseq_id,protein_ensembl_id) REFERENCES Proteins(protein_refseq_id,protein_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS Exons;")
print('Creating the table: Exons')
cur.execute('''
CREATE TABLE Exons(
gene_GeneID_id TEXT,
gene_ensembl_id TEXT,
genomic_start_tx INTEGER,
genomic_end_tx INTEGER,
PRIMARY KEY (gene_GeneID_id, gene_ensembl_id, genomic_start_tx, genomic_end_tx),
FOREIGN KEY(gene_GeneID_id, gene_ensembl_id) REFERENCES Genes(gene_GeneID_id, gene_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS Transcript_Exon;")
print('Creating the table: Transcript_Exon')
cur.execute('''
CREATE TABLE Transcript_Exon(
transcript_refseq_id TEXT,
transcript_ensembl_id TEXT,
order_in_transcript INTEGER,
genomic_start_tx INTEGER,
genomic_end_tx INTEGER,
abs_start_CDS INTEGER,
abs_end_CDS INTEGER,
PRIMARY KEY(transcript_refseq_id, transcript_ensembl_id, order_in_transcript),
FOREIGN KEY(transcript_refseq_id, transcript_ensembl_id)
REFERENCES Transcripts(transcript_refseq_id, transcript_ensembl_id),
FOREIGN KEY(genomic_start_tx, genomic_end_tx)\
REFERENCES Exons(genomic_start_tx, genomic_end_tx)
);'''
)
cur.executescript("DROP TABLE IF EXISTS Proteins;")
print('Creating the table: Proteins')
cur.execute('''
CREATE TABLE Proteins(
protein_refseq_id TEXT,
protein_ensembl_id TEXT,
description TEXT,
synonyms TEXT,
length INTEGER,
transcript_refseq_id TEXT,
transcript_ensembl_id TEXT,
PRIMARY KEY(protein_refseq_id, protein_ensembl_id),
FOREIGN KEY(transcript_refseq_id, transcript_ensembl_id) REFERENCES Transcripts(transcript_refseq_id, transcript_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS DomainType;")
print('Creating the table: DomainType')
cur.execute('''
CREATE TABLE DomainType(
type_id INTEGER NOT NULL PRIMARY KEY UNIQUE,
name TEXT,
other_name TEXT,
description TEXT,
CDD_id TEXT,
cdd TEXT,
pfam TEXT,
smart TEXT,
tigr TEXT,
interpro TEXT
);'''
)
cur.executescript("DROP TABLE IF EXISTS DomainEvent;")
print('Creating the table: DomainEvent')
cur.execute('''
CREATE TABLE DomainEvent(
protein_refseq_id TEXT,
protein_ensembl_id TEXT,
type_id INTEGER,
AA_start INTEGER,
AA_end INTEGER,
nuc_start INTEGER,
nuc_end INTEGER,
total_length INTEGER,
ext_id TEXT,
splice_junction BOOLEAN,
complete_exon BOOLEAN,
PRIMARY KEY(protein_refseq_id, protein_ensembl_id, type_id, AA_start, total_length, ext_id),
FOREIGN KEY(type_id) REFERENCES DomainType(type_id),
FOREIGN KEY(protein_refseq_id, protein_ensembl_id)
REFERENCES Proteins(protein_refseq_id, protein_ensembl_id)
);'''
)
cur.executescript("DROP TABLE IF EXISTS SpliceInDomains;")
print('Creating the table: SpliceInDomains')
cur.execute("""
CREATE TABLE SpliceInDomains(
transcript_refseq_id TEXT,
transcript_ensembl_id TEXT,
exon_order_in_transcript INTEGER,
type_id INTEGER,
total_length INTEGER,
domain_nuc_start INTEGER,
included_len INTEGER,
exon_num_in_domain INTEGER,
PRIMARY KEY (transcript_refseq_id, transcript_ensembl_id, exon_order_in_transcript, type_id,\
total_length, domain_nuc_start),
FOREIGN KEY(transcript_refseq_id, transcript_ensembl_id)
REFERENCES Transcripts(transcript_refseq_id, transcript_ensembl_id),
FOREIGN KEY(exon_order_in_transcript) REFERENCES Transcript_Exon(order_in_transcript),
FOREIGN KEY(type_id) REFERENCES DomainType(type_id),
FOREIGN KEY(domain_nuc_start, total_length) REFERENCES DomainEvent(Nuc_start, total_length)
);"""
)
if merged:
cur.executescript("DROP TABLE IF EXISTS Orthology;")
print('Creating the table: Orthology')
cur.execute("""
CREATE TABLE Orthology(
A_ensembl_id TEXT,
A_GeneSymb TEXT,
A_Species TEXT,
B_ensembl_id TEXT,
B_GeneSymb TEXT,
B_Species TEXT,
PRIMARY KEY (A_ensembl_id, B_ensembl_id),
FOREIGN KEY (A_ensembl_id, B_ensembl_id, A_GeneSymb, B_GeneSymb, A_Species, B_Species)
REFERENCES Genes(gene_ensembl_id, gene_ensembl_id, gene_symbol, gene_symbol, specie, specie)
);"""
)
def create_index(self):
with connect(self.dbName + '.sqlite') as con:
cur = con.cursor()
cur.execute('''CREATE INDEX geneTableIndexBySpecies ON Genes(specie);''')
cur.execute('''CREATE INDEX transcriptTableIndexByGene ON Transcripts(gene_GeneID_id) ;''')
cur.execute(
'''CREATE INDEX exonsInTranscriptsTableIndexByTranscripts ON Transcript_Exon(transcript_refseq_id) ;''')
cur.execute('''CREATE INDEX domainEventsTableIndexByProtein ON DomainEvent(protein_refseq_id) ;''')
cur.execute('''CREATE INDEX domainEventsTableIndexByEnsembl ON DomainEvent(protein_ensembl_id);''')
cur.execute(
'''CREATE INDEX exonInTranscriptsTableIndexByEnsembl ON Transcript_Exon(transcript_ensembl_id);''')
def fill_in_db(self, CollectDomainsFromMerged=True, merged=True, dbName=None):
"""
This function in for unique species. for more than ine use add Species To Merged
"""
if dbName is not None:
self.dbName = dbName
elif merged:
self.dbName = 'DB_merged'
else:
self.dbName = 'DB_' + self.species
if CollectDomainsFromMerged:
self.DomainOrg.collectDatafromDB(self.DomainsSourceDB)
Transcripts = pd.DataFrame(columns=["transcript_refseq_id", "transcript_ensembl_id",
"tx_start", "tx_end", "cds_start", "cds_end", "exon_count",
"gene_GeneID_id", "gene_ensembl_id",
"protein_refseq_id", "protein_ensembl_id"])
Genes = pd.DataFrame(columns=["gene_GeneID_id", "gene_ensembl_id", "gene_symbol", "synonyms",
"chromosome", "strand", "specie"])
Transcript_Exon = pd.DataFrame(columns=["transcript_refseq_id", "transcript_ensembl_id",
"order_in_transcript", "genomic_start_tx",
"genomic_end_tx", "abs_start_CDS", "abs_end_CDS"])
Exons = pd.DataFrame(columns=["gene_GeneID_id", "gene_ensembl_id", "genomic_start_tx", "genomic_end_tx"])
Proteins = pd.DataFrame(columns=["protein_refseq_id", "protein_ensembl_id", "description", "length",
"synonyms", "transcript_refseq_id", "transcript_ensembl_id"])
SpliceInDomains = pd.DataFrame(columns=["transcript_refseq_id", "transcript_ensembl_id",
"exon_order_in_transcript", "domain_nuc_start", "type_id",
"total_length", "included_len", "exon_num_in_domain"])
DomainEvent = pd.DataFrame(columns=["protein_refseq_id", "protein_ensembl_id", "type_id", "AA_start", "AA_end",
"nuc_start", "nuc_end", "total_length", "ext_id",
"splice_junction", "complete_exon"])
DomainType = pd.DataFrame(columns=["type_id", "name", "other_name", "description", "CDD_id", "cdd",
"pfam", "smart", "tigr", "interpro"])
print("Filling in the tables...")
geneSet = set()
uExon = set()
domeve = set()
relevantDomains = set()
for tID, transcript in self.data.Transcripts.items():
ensemblkey = False
if tID.startswith("ENS"):
ensemblkey = True
e_counts = len(transcript.exon_starts)
# insert into Transcripts table
if transcript.CDS is None:
transcript.CDS = transcript.tx
values = (transcript.refseq, transcript.ensembl,) + transcript.tx + transcript.CDS + \
(e_counts, transcript.gene_GeneID, transcript.gene_ensembl,
transcript.protein_refseq, transcript.protein_ensembl,)
idx = len(Transcripts)
Transcripts.loc[idx] = list(values)
# insert into Genes table
if transcript.gene_GeneID not in geneSet and transcript.gene_ensembl not in geneSet:
if ensemblkey:
gene = self.data.Genes[transcript.gene_ensembl]
else:
gene = self.data.Genes[transcript.gene_GeneID]
values = (gene.GeneID, gene.ensembl, gene.symbol,
gene.synonyms, gene.chromosome, gene.strand, self.species,)
idx = len(Genes)
Genes.loc[idx] = list(values)
geneSet.add(gene.GeneID)
geneSet.add(gene.ensembl)
geneSet = geneSet - {None}
start_abs, stop_abs = transcript.exons2abs()
ex_num = 0
starts = transcript.exon_starts.copy()
ends = transcript.exon_ends.copy()
for iEx in range(e_counts):
ex_num += 1
# insert into Transcript_Exon table
values = (transcript.refseq, transcript.ensembl, ex_num, starts[iEx], ends[iEx],
start_abs[iEx], stop_abs[iEx],)
idx = len(Transcript_Exon)
Transcript_Exon.loc[idx] = list(values)
# insert into Exons table
values = (transcript.gene_GeneID, transcript.gene_ensembl, starts[iEx], ends[iEx],)
if values not in uExon:
uExon.add(values)
idx = len(Exons)
Exons.loc[idx] = list(values)
# insert into Proteins table
if ensemblkey:
protID = transcript.protein_ensembl
else:
protID = transcript.protein_refseq
protein = self.data.Proteins[protID]
values = (protein.refseq, protein.ensembl, protein.description, protein.length,
protein.synonyms, transcript.refseq, transcript.ensembl,)
idx = len(Proteins)
Proteins.loc[idx] = list(values)
splicin = set()
for reg in self.data.Domains.get(protID, [None]):
if reg is None:
continue
regID = self.DomainOrg.addDomain(reg)
if regID is None:
continue
relevantDomains.add(regID)
relation, exon_list, length = reg.domain_exon_relationship(start_abs, stop_abs)
total_length = reg.nucEnd - reg.nucStart + 1 # adding one because coordinates are full-closed!
splice_junction = 0
complete = 0
if relation == 'splice_junction':
splice_junction = 1
for i in range(len(exon_list)):
values = (transcript.refseq, transcript.ensembl,
exon_list[i], reg.nucStart, regID,
total_length, length[i], i + 1,)
if values not in splicin:
idx = len(SpliceInDomains)
SpliceInDomains.loc[idx] = list(values)
splicin.add(values)
elif relation == 'complete_exon':
complete = 1
# insert into domain event table
values = (protein.refseq, protein.ensembl, regID,
reg.aaStart, reg.aaEnd, reg.nucStart, reg.nucEnd, total_length,
reg.extID, splice_junction, complete,)
if values not in domeve:
idx = len(DomainEvent)
DomainEvent.loc[idx] = list(values)
domeve.add(values)
bp = time.time()
if merged:
relevantDomains = set(self.DomainOrg.allDomains.keys())
# insert into domain type table
for typeID in relevantDomains:
if typeID in self.DomainOrg.allDomains.keys():
values = (typeID,) + self.DomainOrg.allDomains[typeID]
idx = len(DomainType)
DomainType.loc[idx] = list(values)
print("#### Filling in domain type table: %s seconds" % (time.time() - bp))
with connect(self.dbName + '.sqlite') as con:
Transcripts.to_sql("Transcripts", con, if_exists="append", index=False)
Genes.to_sql("Genes", con, if_exists="append", index=False)
Proteins.to_sql("Proteins", con, if_exists="append", index=False)
Transcript_Exon.to_sql("Transcript_Exon", con, if_exists="append", index=False)
Exons.to_sql("Exons", con, if_exists="append", index=False)
SpliceInDomains.to_sql("SpliceInDomains", con, if_exists="append", index=False)
DomainEvent.to_sql("DomainEvent", con, if_exists="append", index=False)
if merged:
cur.executescript("DROP TABLE IF EXISTS Orthology;")
print('Creating the table: Orthology')
cur.execute("""
CREATE TABLE Orthology(
A_ensembl_id TEXT,
A_GeneSymb TEXT,
A_Species TEXT,
B_ensembl_id TEXT,
B_GeneSymb TEXT,
B_Species TEXT,
PRIMARY KEY (A_ensembl_id, B_ensembl_id),
FOREIGN KEY (A_ensembl_id, B_ensembl_id, A_GeneSymb, B_GeneSymb, A_Species, B_Species)
REFERENCES Genes(gene_ensembl_id, gene_ensembl_id, gene_symbol, gene_symbol, specie, specie)
);"""
)
DomainType.to_sql("DomainType", con, if_exists="append", index=False)
def AddOrthology(self, orthologsDict):
MainOrtho = pd.DataFrame(columns=['A_ensembl_id', 'A_GeneSymb', 'A_Species',
'B_ensembl_id', 'B_GeneSymb', 'B_Species'])
db_data = dict()
species = [spec for x in orthologsDict.keys() for spec in x]
with connect(self.dbName + '.sqlite') as con:
for spec in species:
db_data[spec] = pd.read_sql(
"SELECT gene_ensembl_id,gene_symbol,specie FROM Genes WHERE specie='{}'".format(spec),
con)
print("collecting orthology data for:")
for couple, ortho in orthologsDict.items():
print("\t{} and {}".format(couple[0], couple[1]))
merged_df = None
n = 0
for spec in couple:
db_data[spec]['gene_symbol'] = db_data[spec]['gene_symbol'].str.upper()
db_data[spec].columns = db_data[spec].columns.str.replace('gene_ensembl_id', spec + "_ID")
if n == 0:
merged_df = pd.merge(db_data[spec], ortho)
else:
merged_df = pd.merge(db_data[spec], merged_df)
label = 'A' if n == 0 else 'B'
merged_df.columns = merged_df.columns.str.replace("specie", label + "_Species")
merged_df.columns = merged_df.columns.str.replace("gene_symbol", label + "_GeneSymb")
merged_df.columns = merged_df.columns.str.replace(spec + "_ID", label + "_ensembl_id")
merged_df = merged_df.drop(spec + "_name", axis=1)
n += 1
MainOrtho = MainOrtho.append(merged_df, sort=False)
print("Filling in Orthology table...")
with connect(self.dbName + '.sqlite') as con:
MainOrtho.to_sql("Orthology", con, if_exists="append", index=False)
print("Filling Orthology table complete!")
from tkinter import *
import os
from tkinter.filedialog import *
from tkinter import messagebox
from threading import Thread
from sys import path as systemPath
if __name__ == "__main__":
nope = False
systemPath.insert(1, "src/Scripts/")
from Collector import Collector
collector = Collector(systemPath)
from Loader import Loader
__loader = Loader()
tk = Tk()
tk.withdraw()
tk.overrideredirect(True)
tk.resizable(False, False)
tk.geometry("%dx%d+%d+%d" % (1, 1, 1, 1))
__loader.tk = tk
__loader.collector = collector
from Monitor import Monitor
__screenSize = Monitor().get_screensize()
__loader.screenSize = __screenSize