def findRelated(bed, outFile, cutoff, kinshipFile=None):
bed = leapUtils._fixupBed(bed)
keepArr = leapUtils.findRelated(bed, cutoff, kinshipFile)
if (outFile is not None):
print 'Printing output to', outFile
f = open(outFile, 'w')
for i, (fid, iid) in enumerate(bed.iid):
if (keepArr[i]): f.write(fid + ' ' + iid + ' 0\n')
else: f.write(fid + ' ' + iid + ' 1\n')
f.close()
return keepArr
def findRelated(bed, outFile, cutoff, kinshipFile=None): bed = leapUtils._fixupBed(bed) keepArr = leapUtils.findRelated(bed, cutoff, kinshipFile) if (outFile is not None): print 'Printing output to', outFile f = open(outFile, 'w') for i, (fid,iid) in enumerate(bed.iid): if (keepArr[i]): f.write(fid + ' ' + iid + ' 0\n') else: f.write(fid + ' ' + iid + ' 1\n') f.close() return keepArr
def eigenDecompose(bed, outFile=None): bed = leapUtils._fixupBed(bed) #Compute kinship matrix t0 = time.time() print 'Computing kinship matrix...' XXT = leapUtils.symmetrize(blas.dsyrk(1.0, bed.val, lower=1)) / bed.val.shape[1] print 'Done in %0.2f'%(time.time()-t0), 'seconds' #Compute eigendecomposition S,U = leapUtils.eigenDecompose(XXT) if (outFile is not None): np.savez_compressed(outFile, arr_0=U, arr_1=S, XXT=XXT) eigen = dict([]) eigen['XXT'] = XXT eigen['arr_0'] = U eigen['arr_1'] = S return eigen
def eigenDecompose(bed, outFile):
bed = leapUtils._fixupBed(bed)
# Compute kinship matrix
t0 = time.time()
print "Computing kinship matrix..."
XXT = leapUtils.symmetrize(blas.dsyrk(1.0, bed.val, lower=1)) / bed.val.shape[1]
print "Done in %0.2f" % (time.time() - t0), "seconds"
# Compute eigendecomposition
S, U = leapUtils.eigenDecompose(XXT)
if outFile is not None:
np.savez_compressed(outFile, arr_0=U, arr_1=S, XXT=XXT)
eigen = dict([])
eigen["XXT"] = XXT
eigen["arr_0"] = U
eigen["arr_1"] = S
return eigen
def eigenDecompose(bed, outFile=None):
bed = leapUtils._fixupBed(bed)
#Compute kinship matrix
t0 = time.time()
print 'Computing kinship matrix...'
XXT = leapUtils.symmetrize(blas.dsyrk(1.0, bed.val,
lower=1)) / bed.val.shape[1]
print 'Done in %0.2f' % (time.time() - t0), 'seconds'
#Compute eigendecomposition
S, U = leapUtils.eigenDecompose(XXT)
if (outFile is not None):
np.savez_compressed(outFile, arr_0=U, arr_1=S, XXT=XXT)
eigen = dict([])
eigen['XXT'] = XXT
eigen['arr_0'] = U
eigen['arr_1'] = S
return eigen
