Invalid MF32 section accepted by C++ parser

[gschnabel]

The following MF32/MT151 generated and wrongly formatted section by ChatGPT is accepted by the C++ but not the Python parser:

 2.350439+2 0.000000+0          2          0          1          0 12532151    1
 5.000000-1 0.000000+0          0          0          2          2 12532151    2
 1.000000-4 0.000000+0 0.000000+0 0.000000+0 4.000000-6 0.000000+0 12532151    3
 0.000000+0 0.000000+0 1.000000-4                                  12532151    4
 4.000000-4 0.000000+0 0.000000+0 0.000000+0 2.500000-5 0.000000+0 12532151    5
 0.000000+0 0.000000+0 1.000000-4                                  12532151    6
                                                                   12532  0    0

This command fails:

endf-cli show --no-cpp --ignore_send_records --ignore_missing_tpid /32/151/isotope/1 mf32_test.endf

while this one succeeds:

endf-cli show --ignore_send_records --ignore_missing_tpid /32/151/isotope/1 mf32_test.endf

The following code generated by ChatGPT was used to create the section:

import pandas as pd


def format_endf_float(x: float) -> str:
    if x == 0.0:
        return " 0.000000+0"
    s = f"{x:.6E}"
    mantissa, exp = s.split("E")
    return f"{mantissa}{int(exp):+d}".replace("E", "").rjust(11)

def make_mf32_mlbw(
    df: pd.DataFrame,
    mat: int,
    mt: int,
    awri: float,
    spin: float
) -> str:
    """
    Create MF=32 covariance section for MLBW resonances
    with diagonal covariance only.
    """

    required = {"Er", "Gn", "Gg", "dEr", "dGn", "dGg"}
    if not required.issubset(df.columns):
        raise ValueError(f"DataFrame must contain {required}")

    nres = len(df)
    lines = []

    # -------------------------------
    # HEAD record
    # -------------------------------
    # C1 = AWRI, C2 = 0.0
    # L1 = 2 (MLBW), L2 = 0
    # N1 = NJS (spin groups) = 1
    # N2 = 0
    head = (
        format_endf_float(awri)
        + format_endf_float(0.0)
        + f"{2:11d}{0:11d}{1:11d}{0:11d}"
        + f"{mat:4d}32{mt:3d}    1"
    )
    lines.append(head)

    # -------------------------------
    # Spin group header
    # -------------------------------
    # C1 = spin, C2 = 0
    # L1 = 0, L2 = 0
    # N1 = number of resonances
    # N2 = number of covariance matrices (= Nres)
    sg = (
        format_endf_float(spin)
        + format_endf_float(0.0)
        + f"{0:11d}{0:11d}{nres:11d}{nres:11d}"
        + f"{mat:4d}32{mt:3d}    2"
    )
    lines.append(sg)

    record = 3

    # -------------------------------
    # Covariance matrices
    # -------------------------------
    # Each resonance has a 3x3 diagonal covariance matrix
    # Order: Er, Gn, Gg
    for _, r in df.iterrows():
        cov = [
            r.dEr ** 2, 0.0, 0.0,
            0.0, r.dGn ** 2, 0.0,
            0.0, 0.0, r.dGg ** 2,
        ]

        buf = ""
        for v in cov:
            buf += format_endf_float(v)
            if len(buf) == 66:
                lines.append(
                    buf + f"{mat:4d}32{mt:3d}{record:5d}"
                )
                record += 1
                buf = ""

        if buf.strip():
            lines.append(
                buf.ljust(66) + f"{mat:4d}32{mt:3d}{record:5d}"
            )
            record += 1

    # -------------------------------
    # SEND record
    # -------------------------------
    lines.append(" " * 66 + f"{mat:4d}32  0    0")

    return "\n".join(lines)


df = pd.DataFrame({
    "Er":  [1.0, 10.0],
    "Gn":  [0.02, 0.05],
    "Gg":  [0.10, 0.12],
    "dEr": [0.01, 0.02],
    "dGn": [0.002, 0.005],
    "dGg": [0.01, 0.01],
})

mf32 = make_mf32_mlbw(
    df=df,
    mat=125,
    mt=151,
    awri=235.0439,
    spin=0.5
)

print(mf32)

[gschnabel]

After one iteration with ChatGPT, I get the following MF32 section:

 9.223500+1 2.350439+2          0          0          1          0 12532151    1
 9.223500+4 1.000000+0          0          0          1          0 12532151    2
 1.000000-5 2.000000+7          1          2          0          0 12532151    3
 5.000000-1 0.000000+0          0          0          2          2 12532151    4
 1.000000-4 0.000000+0 0.000000+0 0.000000+0 4.000000-6 0.000000+0 12532151    5
 0.000000+0 0.000000+0 1.000000-4                                  12532151    6
 4.000000-4 0.000000+0 0.000000+0 0.000000+0 2.500000-5 0.000000+0 12532151    7
 0.000000+0 0.000000+0 1.000000-4                                  12532151    8
                                                                   12532  0    0

Which now fails for bot the C++ and Python parser. The updated Python code to generate it was:

import pandas as pd


def format_endf_float(x: float) -> str:
    if x == 0.0:
        return " 0.000000+0"
    s = f"{x:.6E}"
    mantissa, exp = s.split("E")
    return f"{mantissa}{int(exp):+d}".replace("E", "").rjust(11)


import pandas as pd

def make_mf32_mlbw(
    df: pd.DataFrame,
    mat: int,
    mt: int,
    za: float,
    awr: float,
    el: float,
    eh: float,
    spin: float
) -> str:
    """
    Create a minimal MF=32 / MT covariance section for
    Multilevel Breit-Wigner (LRF=2) resonances.
    """

    required = {"Er", "Gn", "Gg", "dEr", "dGn", "dGg"}
    if not required.issubset(df.columns):
        raise ValueError(f"DataFrame must contain {required}")

    nres = len(df)
    lines = []
    seq = 1

    # -------------------------------------------------
    # HEAD: ZA, AWR, NIS
    # -------------------------------------------------
    head = (
        format_endf_float(za)
        + format_endf_float(awr)
        + f"{0:11d}{0:11d}{1:11d}{0:11d}"
        + f"{mat:4d}32{mt:3d}{seq:5d}"
    )
    lines.append(head)
    seq += 1

    # -------------------------------------------------
    # Isotope record: ZAI, ABN, LFW, NER
    # -------------------------------------------------
    isotope = (
        format_endf_float(za * 1000.0)
        + format_endf_float(1.0)
        + f"{0:11d}{0:11d}{1:11d}{0:11d}"
        + f"{mat:4d}32{mt:3d}{seq:5d}"
    )
    lines.append(isotope)
    seq += 1

    # -------------------------------------------------
    # Resonance region record: EL, EH, LRU, LRF, NRO, NAPS
    # -------------------------------------------------
    region = (
        format_endf_float(el)
        + format_endf_float(eh)
        + f"{1:11d}{2:11d}{0:11d}{0:11d}"
        + f"{mat:4d}32{mt:3d}{seq:5d}"
    )
    lines.append(region)
    seq += 1

    # -------------------------------------------------
    # Spin group record
    # C1 = spin, C2 = 0
    # N1 = number of resonances
    # N2 = number of covariance matrices
    # -------------------------------------------------
    spin_group = (
        format_endf_float(spin)
        + format_endf_float(0.0)
        + f"{0:11d}{0:11d}{nres:11d}{nres:11d}"
        + f"{mat:4d}32{mt:3d}{seq:5d}"
    )
    lines.append(spin_group)
    seq += 1

    # -------------------------------------------------
    # Covariance matrices (3x3 diagonal per resonance)
    # Order: Er, Gn, Gg
    # -------------------------------------------------
    for _, r in df.iterrows():
        cov = [
            r.dEr ** 2, 0.0, 0.0,
            0.0, r.dGn ** 2, 0.0,
            0.0, 0.0, r.dGg ** 2,
        ]

        buf = ""
        for v in cov:
            buf += format_endf_float(v)
            if len(buf) == 66:
                lines.append(
                    buf + f"{mat:4d}32{mt:3d}{seq:5d}"
                )
                seq += 1
                buf = ""

        if buf.strip():
            lines.append(
                buf.ljust(66) + f"{mat:4d}32{mt:3d}{seq:5d}"
            )
            seq += 1

    # -------------------------------------------------
    # SEND record
    # -------------------------------------------------
    lines.append(" " * 66 + f"{mat:4d}32  0    0")

    return "\n".join(lines)


df = pd.DataFrame({
    "Er":  [1.0, 10.0],
    "Gn":  [0.02, 0.05],
    "Gg":  [0.10, 0.12],
    "dEr": [0.01, 0.02],
    "dGn": [0.002, 0.005],
    "dGg": [0.01, 0.01],
})

mf32 = make_mf32_mlbw(
    df=df,
    mat=125,
    mt=151,
    za=92.235,
    awr=235.0439,
    el=1e-5,
    eh=2.0e7,
    spin=0.5
)

print(mf32)