def filter_indels(data, superints, edgearr):
    """
    Filter max indels. Needs to split to apply to each read separately.
    The dimensions of superseqs are (chunk, sum(sidx), maxlen).
    """

    maxinds = np.array(data.paramsdict["max_Indels_locus"]).astype(np.int64)

    ## an empty array to fill with failed loci
    ifilter = np.zeros(superints.shape[0], dtype=np.bool_)

    ## if paired then worry about splits
    if "pair" in data.paramsdict["datatype"]:
        for idx in xrange(superints.shape[0]):
            block1 = superints[idx, :, edgearr[idx, 0]:edgearr[idx, 1]]
            block2 = superints[idx, :, edgearr[idx, 2]:edgearr[idx, 3]]

            sums1 = maxind_numba(block1)
            ## If all loci are merged then block2 will be empty which will
            ## cause maxind_numba to throw a very confusing ValueError
            if np.any(block2):
                sums2 = maxind_numba(block2)
            else:
                sums2 = 0

            if (sums1 > maxinds[0]) or (sums2 > maxinds[1]):
                ifilter[idx] = True

    else:
        for idx in xrange(superints.shape[0]):
            ## get block based on edge filters
            block = superints[idx, :, edgearr[idx, 0]:edgearr[idx, 1]]
            ## shorten block to exclude terminal indels
            ## if data at this locus (not already filtered by edges/minsamp)
            if block.shape[1] > 1:
                try:
                    sums = maxind_numba(block)
                except ValueError as inst:
                    msg = "All loci filterd by max_Indels_locus. Try increasing this parameter value."
                    raise IPyradError(msg)
                except Exception as inst:
                    LOGGER.error("error in block {}".format(block))
                #LOGGER.info("maxind numba %s %s", idx, sums)
                #LOGGER.info("sums, maxinds[0], compare: %s %s %s",
                #             sums, maxinds[0], sums > maxinds[0])
                if sums > maxinds[0]:
                    ifilter[idx] = True

    LOGGER.info("--------------maxIndels sums %s", ifilter.sum())
    return ifilter
def filter_indels(data, superints, edgearr):
    """
    Filter max indels. Needs to split to apply to each read separately.
    The dimensions of superseqs are (chunk, sum(sidx), maxlen).
    """

    maxinds = np.array(data.paramsdict["max_Indels_locus"]).astype(np.int64)

    ## an empty array to fill with failed loci
    ifilter = np.zeros(superints.shape[0], dtype=np.bool_)

    ## if paired then worry about splits
    if "pair" in data.paramsdict["datatype"]:
        for idx in xrange(superints.shape[0]):
            block1 = superints[idx, :, edgearr[idx, 0]:edgearr[idx, 1]]
            block2 = superints[idx, :, edgearr[idx, 2]:edgearr[idx, 3]]

            sums1 = maxind_numba(block1)
            ## If all loci are merged then block2 will be empty which will
            ## cause maxind_numba to throw a very confusing ValueError
            if np.any(block2):
                sums2 = maxind_numba(block2)
            else:
                sums2 = 0

            if (sums1 > maxinds[0]) or (sums2 > maxinds[1]):
                ifilter[idx] = True

    else:
        for idx in xrange(superints.shape[0]):
            ## get block based on edge filters
            block = superints[idx, :, edgearr[idx, 0]:edgearr[idx, 1]]
            ## shorten block to exclude terminal indels
            ## if data at this locus (not already filtered by edges/minsamp)
            if block.shape[1] > 1:
                try:
                    sums = maxind_numba(block)
                except ValueError as inst:
                    msg = "All loci filterd by max_Indels_locus. Try increasing this parameter value."
                    raise IPyradError(msg)
                except Exception as inst:
                    LOGGER.error("error in block {}".format(block))
                #LOGGER.info("maxind numba %s %s", idx, sums)
                #LOGGER.info("sums, maxinds[0], compare: %s %s %s",
                #             sums, maxinds[0], sums > maxinds[0])
                if sums > maxinds[0]:
                    ifilter[idx] = True

    LOGGER.info("--------------maxIndels sums %s", ifilter.sum())
    return ifilter
def filter_indels ( data , superints , edgearr ) : maxinds = np . array ( data . paramsdict [ "max_Indels_locus" ] ) . astype ( np . int64 ) ## an empty array to fill with failed loci ifilter = np . zeros ( superints . shape [ 0 ] , dtype = np . bool_ ) ## if paired then worry about splits if "pair" in data . paramsdict [ "datatype" ] : for idx in xrange ( superints . shape [ 0 ] ) : block1 = superints [ idx , : , edgearr [ idx , 0 ] : edgearr [ idx , 1 ] ] block2 = superints [ idx , : , edgearr [ idx , 2 ] : edgearr [ idx , 3 ] ] sums1 = maxind_numba ( block1 ) ## If all loci are merged then block2 will be empty which will ## cause maxind_numba to throw a very confusing ValueError if np . any ( block2 ) : sums2 = maxind_numba ( block2 ) else : sums2 = 0 if ( sums1 > maxinds [ 0 ] ) or ( sums2 > maxinds [ 1 ] ) : ifilter [ idx ] = True else : for idx in xrange ( superints . shape [ 0 ] ) : ## get block based on edge filters block = superints [ idx , : , edgearr [ idx , 0 ] : edgearr [ idx , 1 ] ] ## shorten block to exclude terminal indels ## if data at this locus (not already filtered by edges/minsamp) if block . shape [ 1 ] > 1 : try : sums = maxind_numba ( block ) except ValueError as inst : msg = "All loci filterd by max_Indels_locus. Try increasing this parameter value." raise IPyradError ( msg ) except Exception as inst : LOGGER . error ( "error in block {}" . format ( block ) ) #LOGGER.info("maxind numba %s %s", idx, sums) #LOGGER.info("sums, maxinds[0], compare: %s %s %s", #             sums, maxinds[0], sums > maxinds[0]) if sums > maxinds [ 0 ] : ifilter [ idx ] = True LOGGER . info ( "--------------maxIndels sums %s" , ifilter . sum ( ) ) return ifilter
Filter max indels. Needs to split to apply to each read separately.
    The dimensions of superseqs are (chunk, sum(sidx), maxlen).
Filter max indels . Needs to split to apply to each read separately . The dimensions of superseqs are ( chunk sum ( sidx ) maxlen ) .