def Collect(infile,
            with_headers=False,
            annotator_format=False,
            use_annotator_fdr=False,
            delims="",
            ignore="",
            max_pvalue=1.0,
            max_qvalue=None):

    data = []

    lines = filter(lambda x: x[0] != "#", infile.readlines())

    if len(lines) == 0:
        return data

    if with_headers:
        del lines[0]

    if annotator_format:

        lines = [line for line in lines if not line.startswith("Iteration")]
        annotator_fdr = {}
        annotator_level = None
        for line in lines:
            if len(line) == 1:
                continue                  # skip trailing blank lines

            if line.startswith("--"):
                if line.startswith("-- False"):
                    annotator_level = float(
                        re.search("-- False Discovery summary for p-value (.+):", line).groups()[0])
                    annotator_fdr[annotator_level] = {}
                elif line.startswith("--  Category"):
                    pass
                else:
                    if re.search("insufficiently", line):
                        continue
                    dd = re.split("\s+", line[4:-1])
                    d = DataFDR()
                    d.mObserved, d.mAverage, d.mMedian, d.m95 = map(
                        float, dd[1:])
                    annotator_fdr[annotator_level][dd[0]] = d
                continue
            else:
                if line[0] == "Z":
                    continue  # skip header
                if len(line[:-1].split('\t')) != 9:
                    continue  # HACK: accounts for a bug in Annotator output

                try:
                    (z, percentchange, pvalue, observed, expected, low95,
                     up95, stddev, description) = line[:-1].split('\t')[:9]
                except ValueError:
                    raise ValueError("# parsing error in line: %s" % line[:-1])

            d = DataPoint()
            d.mAnnotation = description
            d.mPValue = float(pvalue)
            d.mFoldChange = 1.0 + float(percentchange) / 100.0
            data.append(d)
    else:

        for line in lines:
            try:
                (code, goid, scount, stotal, spercent, bcount, btotal, bpercent, ratio,
                 pover, punder, goid, category, description) = line[:-1].split("\t")[:14]
            except ValueError:
                raise ValueError("# parsing error in line: %s" % line[:-1])

            if code == "+":
                p = pover
            else:
                p = punder

            d = DataPoint()
            d.mAnnotation = description
            d.mPValue = float(p)
            d.mFoldChange = float(spercent) / float(bpercent)
            data.append(d)

    # apply filters
    for c in delims:
        for d in data:
            d.mAnnotation = d.mAnnotation.split(c)[0]
    for c in ignore:
        for d in data:
            d.mAnnotation = d.mAnnotation.replace(c, '')

    ninput = len(data)
    no_fdr = False
    # apply filters

    if ninput > 0:
        if max_qvalue is not None:
            if use_annotator_fdr:
                pvalues = annotator_fdr.keys()
                pvalues.sort()
                pvalues.reverse()
                for pvalue in pvalues:
                    try:
                        d = annotator_fdr[pvalue]["Significant"]
                    except KeyError:
                        continue
                    if d.mObserved == 0:
                        E.info("no data remaining after fdr filtering")
                        data = []
                        break
                    elif d.mAverage / d.mObserved < max_qvalue:
                        E.info("filtering with P-value of %f" % pvalue)
                        data = [x for x in data if x.mPValue < pvalue]
                        break
                else:
                    E.warn("fdr could not be computed - compute more "
                           "samples (at P = %f, actual fdr=%f)" %
                           (pvalue, d.mAverage / d.mObserved))
                    no_fdr = True

            if no_fdr:
                if use_annotator_fdr:
                    E.info("estimating FDR from observed P-Values")

                pvalues = [x.mPValue for x in data]
                vlambda = numpy.arange(0, max(pvalues), 0.05)
                try:
                    qvalues = Stats.doFDR(
                        pvalues, vlambda=vlambda, fdr_level=max_qvalue)
                except ValueError, msg:
                    E.warn(
                        "fdr could not be computed - no filtering: %s" % msg)
                    no_fdr = True
                else:
                    data = [x[0] for x in zip(data, qvalues.mPassed) if x[1]]
        elif max_pvalue is not None:
            data = [x for x in data if x.mPValue < max_pvalue]

    if no_fdr:
        data = []

    nremoved = ninput - len(data)

    return data, nremoved, no_fdr

# some definitions for the layout of the picture
read input table.
dataset/ETHPy150Open CGATOxford/cgat/scripts/go2svg.py/Collect