How to use the ecl2df.common.parse_opmio_deckrecord function in ecl2df

steplist = parse_opmio_tstep_rec(rec)
                # Assuming not LAB units, then the unit is days.
                days = sum(steplist)
                date += datetime.timedelta(days=days)
                logger.info(
                    "Advancing %s days to %s through TSTEP", str(days), str(date)
                )
        elif kword.name == "COMPDAT":
            for rec in kword:  # Loop over the lines inside COMPDAT record
                rec_data = parse_opmio_deckrecord(
                    rec, "COMPDAT", renamer=COMPDAT_RENAMER
                )
                rec_data["DATE"] = date
                compdatrecords.append(rec_data)
        elif kword.name == "COMPSEGS":
            wellname = parse_opmio_deckrecord(
                kword[0], "COMPSEGS", itemlistname="records", recordindex=0
            )["WELL"]
            for recidx in range(1, len(kword)):
                rec = kword[recidx]
                rec_data = parse_opmio_deckrecord(
                    rec, "COMPSEGS", itemlistname="records", recordindex=1
                )
                rec_data["WELL"] = wellname
                rec_data["DATE"] = date
                compsegsrecords.append(rec_data)
        elif kword.name == "WELSEGS":
            # First record contains meta-information for well
            # (opm deck returns default values for unspecified items.)
            welsegsdict = parse_opmio_deckrecord(
                kword[0], "WELSEGS", itemlistname="records", recordindex=0
            )

if kword.name == "GRUPTREE":
            found_gruptree = True
            for edgerec in kword:
                edge_dict = parse_opmio_deckrecord(edgerec, "GRUPTREE")
                currentedges[
                    (edge_dict["CHILD_GROUP"], edge_dict["PARENT_GROUP"])
                ] = "GRUPTREE"
        if kword.name == "WELSPECS" and welspecs:
            found_welspecs = True
            for wellrec in kword:
                wspc_dict = parse_opmio_deckrecord(wellrec, "WELSPECS")
                currentedges[(wspc_dict["WELL"], wspc_dict["GROUP"])] = "WELSPECS"
        if kword.name == "GRUPNET":
            found_grupnet = True
            for rec in kword:
                grupnet_data = parse_opmio_deckrecord(rec, "GRUPNET")
                grupnetrecords.append(grupnet_data)
            grupnet_df = (
                pd.DataFrame(grupnetrecords)
                .drop_duplicates(subset="NAME", keep="last")
                .set_index("NAME")
            )

    # Ensure we also store any tree information found after the last DATE statement
    if found_gruptree or found_welspecs:
        for edgename, value in currentedges.items():
            rec_dict = {
                "DATE": date,
                "CHILD": edgename[0],
                "PARENT": edgename[1],
                "KEYWORD": value,
            }

deck (opm.io Deck or EclFiles): Eclipse deck
    """
    if isinstance(deck, EclFiles):
        deck = deck.get_ecldeck()

    # In[91]: list(deck['FAULTS'][0])
    # Out[91]: [[u'F1'], [36], [36], [41], [42], [1], [14], [u'I']]
    data = []
    # It is allowed in Eclipse to use the keyword FAULTS
    # as many times as needed. Thus we need to loop in some way:
    for keyword in deck:
        if keyword.name == "FAULTS":
            for rec in keyword:
                # Each record now has a range potentially in three
                # dimensions for the fault, unroll this:
                frec_dict = parse_opmio_deckrecord(rec, "FAULTS")
                faultname = frec_dict["NAME"]
                faultface = frec_dict["FACE"]
                for i_idx in range(frec_dict["IX1"], frec_dict["IX2"] + 1):
                    for j_idx in range(frec_dict["IY1"], frec_dict["IY2"] + 1):
                        for k_idx in range(frec_dict["IZ1"], frec_dict["IZ2"] + 1):
                            data.append([faultname, i_idx, j_idx, k_idx, faultface])
    return pd.DataFrame(columns=COLUMNS, data=data)

compsegsrecords.append(rec_data)
        elif kword.name == "WELSEGS":
            # First record contains meta-information for well
            # (opm deck returns default values for unspecified items.)
            welsegsdict = parse_opmio_deckrecord(
                kword[0], "WELSEGS", itemlistname="records", recordindex=0
            )
            # Loop over all subsequent records.
            for recidx in range(1, len(kword)):
                rec = kword[recidx]
                # WARNING: We assume that SEGMENT1 === SEGMENT2 (!!!) (if not,
                # we need to loop over a range just as for layer in compdat)
                rec_data = welsegsdict.copy()
                rec_data["DATE"] = date
                rec_data.update(
                    parse_opmio_deckrecord(
                        rec, "WELSEGS", itemlistname="records", recordindex=1
                    )
                )
                if "INFO_TYPE" in rec_data and rec_data["INFO_TYPE"] == "ABS":
                    rec_data["SEGMENT_MD"] = rec_data["SEGMENT_LENGTH"]
                welsegsrecords.append(rec_data)
        elif kword.name == "TSTEP":
            logger.warning("Possible premature stop at first TSTEP")
            break

    compdat_df = pd.DataFrame(compdatrecords)

    if unroll and not compdat_df.empty:
        compdat_df = unrolldf(compdat_df, "K1", "K2")

    compsegs_df = pd.DataFrame(compsegsrecords)

"Advancing %s days to %s through TSTEP", str(days), str(date)
                    )
            else:
                logger.critical("BUG: Should not get here")
                return pd.DataFrame()
        if kword.name == "GRUPTREE":
            found_gruptree = True
            for edgerec in kword:
                edge_dict = parse_opmio_deckrecord(edgerec, "GRUPTREE")
                currentedges[
                    (edge_dict["CHILD_GROUP"], edge_dict["PARENT_GROUP"])
                ] = "GRUPTREE"
        if kword.name == "WELSPECS" and welspecs:
            found_welspecs = True
            for wellrec in kword:
                wspc_dict = parse_opmio_deckrecord(wellrec, "WELSPECS")
                currentedges[(wspc_dict["WELL"], wspc_dict["GROUP"])] = "WELSPECS"
        if kword.name == "GRUPNET":
            found_grupnet = True
            for rec in kword:
                grupnet_data = parse_opmio_deckrecord(rec, "GRUPNET")
                grupnetrecords.append(grupnet_data)
            grupnet_df = (
                pd.DataFrame(grupnetrecords)
                .drop_duplicates(subset="NAME", keep="last")
                .set_index("NAME")
            )

    # Ensure we also store any tree information found after the last DATE statement
    if found_gruptree or found_welspecs:
        for edgename, value in currentedges.items():
            rec_dict = {

# Assuming not LAB units, then the unit is days.
                    days = sum(steplist)
                    if days <= 0:
                        logger.critical("Invalid TSTEP, summed to %s days", str(days))
                        return pd.DataFrame()
                    date += datetime.timedelta(days=days)
                    logger.info(
                        "Advancing %s days to %s through TSTEP", str(days), str(date)
                    )
            else:
                logger.critical("BUG: Should not get here")
                return pd.DataFrame()
        if kword.name == "GRUPTREE":
            found_gruptree = True
            for edgerec in kword:
                edge_dict = parse_opmio_deckrecord(edgerec, "GRUPTREE")
                currentedges[
                    (edge_dict["CHILD_GROUP"], edge_dict["PARENT_GROUP"])
                ] = "GRUPTREE"
        if kword.name == "WELSPECS" and welspecs:
            found_welspecs = True
            for wellrec in kword:
                wspc_dict = parse_opmio_deckrecord(wellrec, "WELSPECS")
                currentedges[(wspc_dict["WELL"], wspc_dict["GROUP"])] = "WELSPECS"
        if kword.name == "GRUPNET":
            found_grupnet = True
            for rec in kword:
                grupnet_data = parse_opmio_deckrecord(rec, "GRUPNET")
                grupnetrecords.append(grupnet_data)
            grupnet_df = (
                pd.DataFrame(grupnetrecords)
                .drop_duplicates(subset="NAME", keep="last")