def raw_squiggle_search3(squiggle,hashthang):
result=[]
for ref in hashthang:
try:
queryarray=scale(squiggle)
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Fprimewin'])
result.append((dist,ref,"F",path[1][0],path[1][-1],path[0][0],path[0][-1],cost.mean()))
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Rprimewin'])
result.append((dist,ref,"R",(len(hashthang[ref]['Rprimewin'])-path[1][-1]),(len(hashthang[ref]['Rprimewin'])-path[1][0]),path[0][0],path[0][-1],cost.mean()))
except Exception,err:
print "Warp Fail"
def squiggle_search2(squiggle,kmerhash,seqlen): result=[] for ref in kmerhash: #print "ss2",ref queryarray = sklearn.preprocessing.scale(np.array(squiggle),axis=0,with_mean=True,with_std=True,copy=True) dist, cost, path = mlpy.dtw_subsequence(queryarray,kmerhash[ref]['Fprime']) result.append((dist,ref,"F",path[1][0],ref,path[1][-1])) dist, cost, path = mlpy.dtw_subsequence(queryarray,kmerhash[ref]['Rprime']) result.append((dist,ref,"R",path[1][0],ref,path[1][-1])) return sorted(result,key=lambda result: result[0])[0][1],sorted(result,key=lambda result: result[0])[0][0],sorted(result,key=lambda result: result[0])[0][2],sorted(result,key=lambda result: result[0])[0][3],sorted(result,key=lambda result: result[0])[0][4],sorted(result,key=lambda result: result[0])[0][5]
def raw_squiggle_search2(squiggle,hashthang):
result=[]
#print args.speedmode
for ref in hashthang:
try:
queryarray = scale(squiggle)
mx = np.max(queryarray)
scalingFactor = 1 # iqr # 3 # 1.2 # MS
queryarray *= scalingFactor
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Fprime'])
result.append((dist,ref,"F",path[1][0],path[1][-1],path[0][0],path[0][-1]))
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Rprime'])
result.append((dist,ref,"R",(len(hashthang[ref]['Rprime'])-path[1][-1]),(len(hashthang[ref]['Rprime'])-path[1][0]),path[0][0],path[0][-1]))
except Exception,err:
print "Warp Fail"
result.append((distance,id,"F",location,ref))
subjectfile = id+"_"+str(ref)+"_"+"R"+"_subject.txt"
subjectfile = re.sub('\|','_',subjectfile)
#seqlen2 = str(seqlen[id])
commands = subjectfile+' '+queryfile+' 200'+' 0.1'
#print commands
#current = str(multiprocessing.current_process())
#currentnum=int(re.search(r'\d+', current).group())
gpucode=str()
#if (currentnum % 2 == 0):
#print "Even"
gpucode='./UCR_DTW '
#else:
# #print "Odd"
# gpucode='./GPU-DTW '
runcommand = gpucode+commands
def raw_squiggle_search2(squiggle,hashthang):
result=[]
for ref in hashthang:
try:
memory_usage_psutil()
queryarray = sklearn.preprocessing.scale(np.array(squiggle),axis=0,with_mean=True,with_std=True,copy=True)
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Fprime'])
memory_usage_psutil()
result.append((dist,ref,"F",path[1][0],path[1][-1],path[0][0],path[0][-1],cost,path))
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Rprime'])
result.append((dist,ref,"R",(len(hashthang[ref]['Rprime'])-path[1][-1]),(len(hashthang[ref]['Rprime'])-path[1][0]),path[0][0],path[0][-1],cost,path))
memory_usage_psutil()
except Exception,err:
print "Warp Fail"
return sorted(result,key=lambda result: result[0])[0][1],sorted(result,key=lambda result: result[0])[0][0],sorted(result,key=lambda result: result[0])[0][2],sorted(result,key=lambda result: result[0])[0][3],sorted(result,key=lambda result: result[0])[0][4],sorted(result,key=lambda result: result[0])[0][5],sorted(result,key=lambda result: result[0])[0][6],sorted(result,key=lambda result: result[0])[0][7],sorted(result,key=lambda result: result[0])[0][8]
def raw_squiggle_search2(squiggle,hashthang):
result=[]
for ref in hashthang:
try:
if (args.verbose is True):
memory_usage_psutil()
queryarray = sklearn.preprocessing.scale(np.array(squiggle),axis=0,with_mean=True,with_std=True,copy=True)
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Fprime'])
if (args.verbose is True):
memory_usage_psutil()
result.append((dist,ref,"F",path[1][0],path[1][-1],path[0][0],path[0][-1],cost,path))
dist, cost, path = mlpy.dtw_subsequence(queryarray,hashthang[ref]['Rprime'])
result.append((dist,ref,"R",(len(hashthang[ref]['Rprime'])-path[1][-1]),(len(hashthang[ref]['Rprime'])-path[1][0]),path[0][0],path[0][-1],cost,path))
if (args.verbose is True):
memory_usage_psutil()
except Exception,err:
print "Warp Fail"
def getDistance(self, songPath, songName, dbPath):
temp = []
manager = midimanager.MidiManager()
record = manager.segmentNote(songPath, songName + '_result.csv')
# print 'Searching ' + songName + ' in database...'
for fileName in os.listdir(dbPath):
if fileName.endswith('.csv'):
reference = manager.readMidi(dbPath, fileName)
dist, cost, path = mlpy.dtw_subsequence(record, reference)
temp.append([int(dist), fileName[:-10]])
return temp
def squiggle_search2_old(squiggle, kmerhash, seqlen):
result = []
for ref in kmerhash:
# print "ss2",ref
queryarray = sklearn.preprocessing.scale(np.array(squiggle), axis=0, with_mean=True, with_std=True, copy=True)
dist, cost, path = mlpy.dtw_subsequence(queryarray, kmerhash[ref]["Fprime"])
result.append((dist, ref, "F", path[1][0], ref, path[1][-1]))
dist, cost, path = mlpy.dtw_subsequence(queryarray, kmerhash[ref]["Rprime"])
result.append((dist, ref, "R", path[1][0], ref, path[1][-1]))
# ('J02459', 41.017514495176989, 'F', 10003, 'J02459', 10198)
# distanceR,seqmatchnameR,frR,rsR,reR,qsR,qeR=sorted(result,key=lambda result: result[0])[0]
# return seqmatchnameR,distanceR,frR,rsR,reR,qsR,qeR
return (
sorted(result, key=lambda result: result[0])[0][1],
sorted(result, key=lambda result: result[0])[0][0],
sorted(result, key=lambda result: result[0])[0][2],
sorted(result, key=lambda result: result[0])[0][3],
sorted(result, key=lambda result: result[0])[0][4],
sorted(result, key=lambda result: result[0])[0][5],
)
def squiggle_search2(squiggle,channel_id,read_id,args,seqids,threedarray,seqlen):
'''
This function matches an incoming squiggle to a reference. Coordinates are returned with respect to the forward
strand only. This is important to recall for subsequent downstream processing. Thus a read which is reported as
mapping to the reverse strand will report its coodinates on the forward strand.
'''
result=[]
blocksize=200000
overlap=blocksize-500
for ref in seqids:
refid=seqids.index(ref)
Rprime,Fprime=threedarray[refid]
#queryarray = sklearn.preprocessing.scale(np.array(squiggle),axis=0,with_mean=True,with_std=True,copy=True)
queryarray = sklearn.preprocessing.scale(np.array(squiggle,dtype=float),axis=0,with_mean=True,with_std=True,copy=True)
refsubset = Fprime
indexes = np.array(xrange(len(refsubset)))
subrefs = [refsubset[i:i+blocksize]for i in indexes[::overlap]]
for blockid,ref_ in enumerate(subrefs):
#current = multiprocessing.current_process()
tic = time.time()
dist, cost, path = mlpy.dtw_subsequence(queryarray,ref_)
#result.append((dist,ref,"F",path[1][0],path[1][-1],path[0][0],path[0][-1]))
result.append((dist,ref,"F",path[1][0]+(blockid*overlap),path[1][-1]+(blockid*overlap),path[0][0],path[0][-1]))
#print "Blockid", blockid, time.time()-tic
refsubset = Rprime
subrefs = [refsubset[i:i+blocksize]for i in indexes[::overlap]]
for blockid,ref_ in enumerate(subrefs):
#print "Blockid", blockid, time.time()
dist, cost, path = mlpy.dtw_subsequence(queryarray,ref_)
#result.append((dist,ref,"R",path[1][0]+(blockid*overlap),ref))
#result.append((dist,ref,"R",path[1][0]+(blockid*overlap),path[1][-1]+(blockid*overlap),path[0][0],path[0][-1]))
#result.append((dist,ref,"R",(len(Rprime)-(path[1][-1]+(blockid*overlap))),(len(Rprime)-(path[1][0]+(blockid*overlap))),path[0][0],path[0][-1]))
#Corrected for the fact that this is a reverse complement
result.append((dist,ref,"R",(len(Rprime)-(path[1][0]+(blockid*overlap))),(len(Rprime)-(path[1][-1]+(blockid*overlap))),path[0][0],path[0][-1]))
# Note first two elements flipped for return deliberately.
distanceR,seqmatchnameR,frR,rsR,reR,qsR,qeR=sorted(result,key=lambda result: result[0])[0]
return seqmatchnameR,distanceR,frR,rsR,reR,qsR,qeR
testarray = kmerhash2[id][ref]['Rprime']
filename = id+"_"+str(ref)+"_R_subject.bin"
filename = re.sub('\|','_',filename)
with open(filename, "wb") as f:
f.write(ar.array("f", testarray))
filename = id+"_"+str(ref)+"_R_subject.txt"
filename = re.sub('\|','_',filename)
np.savetxt(filename, testarray, delimiter=',')
#ecit
db = MySQLdb.connect(host=dbhost, user=dbusername, passwd=dbpass, port=dbport)
cursor = db.cursor()
#sql = "use minion_PLSP57501_2014_10_10_DSmin1_run2_LambdaSK002_5041"
#sql = "use minion_PLSP57501_20140909_JA_defA_4434"
sql = "use minion_LomanLabz_013731_11rx_v2_3135"
print sql
cursor.execute(sql)
numbers = range(0,10)
for number in numbers:
#print number
sql = "SELECT basename_id ,pos,flag, channel, read_id,tracking_id.basename,file_path FROM caller_basecalled_template_%s inner join align_sam_basecalled_2d using (basename_id) inner join config_general using (basename_id) inner join tracking_id using (basename_id) group by basename_id limit 1" %(number)
#sql = "SELECT basename_id , channel, read_id FROM caller_basecalled_template_%s inner join config_general using (basename_id) where basename_id not in (select basename_id from align_sam_basecalled_template) group by basename_id" %(number)
print sql
import sys, os, re
if(retcode is not None): break ####################################################################### def squiggle_search2(squiggle,kmerhash2,channel_id,read_id,seqlen):
#!/usr/bin/env python
#!C:\anaconda python
with open("bench_log.txt", "a") as text_file:
text_file.write("\n" + str(amp + 1) + "," + str(window + 1) +
",mlpy," + str(mlpystddist) + "," +
str(timet.microseconds) + ',' +
str(mlpystdpath[1][0] + count) + ',' +
str(mlpystdpath[1][-1] + count))
path1 = np.savetxt('paths/' + "amp_" + str(amp + 1) + "_window_" +
str(window + 1) + '_query_mlpy.txt',
mlpystdpath[0],
delimiter=',')
path2 = np.savetxt('paths/' + "amp_" + str(amp + 1) + "_window_" +
str(window + 1) + '_ref_mlpy.txt',
mlpystdpath[1],
delimiter=',')
timeb = datetime.now()
mlpysubdist, mlpysubcost, mlpysubpath = mlpy.dtw_subsequence(x, y)
timet = datetime.now() - timeb
print("mlpy sub complete on amp " + str(amp + 1))
with open("bench_log.txt", "a") as text_file:
text_file.write("\n" + str(amp + 1) + "," + str(window + 1) +
",mlpy_sub," + str(mlpysubdist) + "," +
str(timet.microseconds) + ',' +
str(mlpysubpath[1][0] + count) + ',' +
str(mlpysubpath[1][-1] + count))
path1 = np.savetxt('paths/' + "amp_" + str(amp + 1) + "_window_" +
str(window + 1) + '_query_mlpysub.txt',
mlpysubpath[0],
delimiter=',')
path2 = np.savetxt('paths/' + "amp_" + str(amp + 1) + "_window_" +
str(window + 1) + '_ref_mlpysub.txt',
mlpysubpath[1],
def dtwSubsequence(x, y):
return mlpy.dtw_subsequence(x, y)
def dtwCalculate(x, y):
dist, cost, path = mlpy.dtw_subsequence(x, y)
path = path[1]
return dist, path[len(path) - 1]
#!/usr/bin/env python
import sys, os, re
]
refLen = len(ref_Ft)
#frm_ = refLen - frm
#to_ = refLen - to
#------------------------------------------------------------------------------
# DTW ...
import mlpy
def dtw(qry, (ref_F, ref_R)):
if len(qry) is 0 : return '-1', -1
dist_F, _, path_F = mlpy.dtw_subsequence(qry, ref_F)
dist_R, _, path_R = mlpy.dtw_subsequence(qry, ref_R)
if dist_F < dist_R: return 'F', path_F[1][0] - offset
else: return 'R', refLen - path_R[1][0] - ampSz + offset
#------------------------------------------------------------------------------
# Generate Synthectic Read...
def generateRead():
sz = ampSz
trg = np.random.randint(0, refLen-sz, 1)[0]
# Using t_model ...
i,j = trg, trg+sz # NB ascending numerically
#print trg, i, j
def map(self, ref_event_levels, query_events):
self.ref_event_levels = ref_event_levels
self.query_events = query_events
result = mlpy.dtw_subsequence(self.scaled_query_event_currents, self.scaled_reference_event_currents)
return MapResults(result)
