def nworker(data, smpchunk, tests):
    """ The workhorse function. Not numba. """
    
    ## tell engines to limit threads
    #numba.config.NUMBA_DEFAULT_NUM_THREADS = 1
    
    ## open the seqarray view, the modified array is in bootsarr
    with h5py.File(data.database.input, 'r') as io5:
        seqview = io5["bootsarr"][:]
        maparr = io5["bootsmap"][:]

    ## create an N-mask array of all seq cols (this isn't really too slow)
    nall_mask = seqview[:] == 78

    ## tried numba compiling everythign below here, but was not faster
    ## than making nmask w/ axis arg in numpy

    ## get the input arrays ready
    rquartets = np.zeros((smpchunk.shape[0], 4), dtype=np.uint16)
    rweights = None
    #rweights = np.ones(smpchunk.shape[0], dtype=np.float64)
    rdstats = np.zeros((smpchunk.shape[0], 4), dtype=np.uint32)

    #times = []
    ## fill arrays with results using numba funcs
    for idx in xrange(smpchunk.shape[0]):
        ## get seqchunk for 4 samples (4, ncols) 
        sidx = smpchunk[idx]
        seqchunk = seqview[sidx]

        ## get N-containing columns in 4-array, and invariant sites.
        nmask = np.any(nall_mask[sidx], axis=0)
        nmask += np.all(seqchunk == seqchunk[0], axis=0)  ## <- do we need this?

        ## get matrices if there are any shared SNPs
        ## returns best-tree index, qscores, and qstats
        #bidx, qscores, qstats = calculate(seqchunk, maparr[:, 0], nmask, tests)
        bidx, qstats = calculate(seqchunk, maparr[:, 0], nmask, tests)
        
        ## get weights from the three scores sorted. 
        ## Only save to file if the quartet has information
        rdstats[idx] = qstats 
        rquartets[idx] = smpchunk[idx][bidx]

    return rquartets, rweights, rdstats
def nworker(data, smpchunk, tests):
    """ The workhorse function. Not numba. """
    
    ## tell engines to limit threads
    #numba.config.NUMBA_DEFAULT_NUM_THREADS = 1
    
    ## open the seqarray view, the modified array is in bootsarr
    with h5py.File(data.database.input, 'r') as io5:
        seqview = io5["bootsarr"][:]
        maparr = io5["bootsmap"][:]

    ## create an N-mask array of all seq cols (this isn't really too slow)
    nall_mask = seqview[:] == 78

    ## tried numba compiling everythign below here, but was not faster
    ## than making nmask w/ axis arg in numpy

    ## get the input arrays ready
    rquartets = np.zeros((smpchunk.shape[0], 4), dtype=np.uint16)
    rweights = None
    #rweights = np.ones(smpchunk.shape[0], dtype=np.float64)
    rdstats = np.zeros((smpchunk.shape[0], 4), dtype=np.uint32)

    #times = []
    ## fill arrays with results using numba funcs
    for idx in xrange(smpchunk.shape[0]):
        ## get seqchunk for 4 samples (4, ncols) 
        sidx = smpchunk[idx]
        seqchunk = seqview[sidx]

        ## get N-containing columns in 4-array, and invariant sites.
        nmask = np.any(nall_mask[sidx], axis=0)
        nmask += np.all(seqchunk == seqchunk[0], axis=0)  ## <- do we need this?

        ## get matrices if there are any shared SNPs
        ## returns best-tree index, qscores, and qstats
        #bidx, qscores, qstats = calculate(seqchunk, maparr[:, 0], nmask, tests)
        bidx, qstats = calculate(seqchunk, maparr[:, 0], nmask, tests)
        
        ## get weights from the three scores sorted. 
        ## Only save to file if the quartet has information
        rdstats[idx] = qstats 
        rquartets[idx] = smpchunk[idx][bidx]

    return rquartets, rweights, rdstats
def nworker ( data , smpchunk , tests ) : ## tell engines to limit threads #numba.config.NUMBA_DEFAULT_NUM_THREADS = 1 ## open the seqarray view, the modified array is in bootsarr with h5py . File ( data . database . input , 'r' ) as io5 : seqview = io5 [ "bootsarr" ] [ : ] maparr = io5 [ "bootsmap" ] [ : ] ## create an N-mask array of all seq cols (this isn't really too slow) nall_mask = seqview [ : ] == 78 ## tried numba compiling everythign below here, but was not faster ## than making nmask w/ axis arg in numpy ## get the input arrays ready rquartets = np . zeros ( ( smpchunk . shape [ 0 ] , 4 ) , dtype = np . uint16 ) rweights = None #rweights = np.ones(smpchunk.shape[0], dtype=np.float64) rdstats = np . zeros ( ( smpchunk . shape [ 0 ] , 4 ) , dtype = np . uint32 ) #times = [] ## fill arrays with results using numba funcs for idx in xrange ( smpchunk . shape [ 0 ] ) : ## get seqchunk for 4 samples (4, ncols)  sidx = smpchunk [ idx ] seqchunk = seqview [ sidx ] ## get N-containing columns in 4-array, and invariant sites. nmask = np . any ( nall_mask [ sidx ] , axis = 0 ) nmask += np . all ( seqchunk == seqchunk [ 0 ] , axis = 0 ) ## <- do we need this? ## get matrices if there are any shared SNPs ## returns best-tree index, qscores, and qstats #bidx, qscores, qstats = calculate(seqchunk, maparr[:, 0], nmask, tests) bidx , qstats = calculate ( seqchunk , maparr [ : , 0 ] , nmask , tests ) ## get weights from the three scores sorted.  ## Only save to file if the quartet has information rdstats [ idx ] = qstats rquartets [ idx ] = smpchunk [ idx ] [ bidx ] return rquartets , rweights , rdstats
The workhorse function. Not numba.
The workhorse function . Not numba .