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`petab_files_1to2` keeps a stale model file when converting into a non-empty output directory

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評估

難度
2/5
預估耗時
1-3 小時
新手友好度
73/100
Issue 類型
缺陷
描述清晰度
描述清楚
活躍度
活躍
技術堆疊
python
領域
backend

研究方向

從 petab/v2/petab1to2.py 中的 _copy_file 開始，追蹤 petab1to2 如何處理輸出目錄，包括現有的 experiments.tsv 情況。使用 issue 中的重現程式碼檢查重複轉換至非空目錄的情況。選定的行為保持一致即代表完成：以 ValueError 拒絕非空輸出目錄，或覆寫現有模型檔案，除非來源與目的地是同一個檔案。

由索引模型根據 Issue 內容生成。

描述

petab_files_1to2 (and petab1to2 with output_dir) doesn't overwrite an existing model file in the output directory. The other tables are overwritten, so the result mixes the new tables with the old model, without any warning.

The cause is _copy_file in petab/v2/petab1to2.py (since #328):

try:
    if dest.samefile(src):
        return
except FileNotFoundError:
    shutil.copy(str(src), str(dest))

If dest exists and is a different file, samefile returns False and nothing is copied.

Reproducer (two v1 problems that differ only in the model's value of k1, converted into the same directory):

import tempfile
from pathlib import Path

import pandas as pd

from petab import v1
from petab.v1.models.sbml_model import SbmlModel
from petab.v2 import Problem
from petab.v2.petab1to2 import petab1to2


def write_v1(path: Path, k1: float) -> Path:
    return v1.Problem(
        model=SbmlModel.from_antimony(f"A = 1; B = 0; k1 = {k1}; A -> B; k1 * A"),
        condition_df=v1.get_condition_df(pd.DataFrame({"conditionId": ["c1"]})),
        observable_df=v1.get_observable_df(
            pd.DataFrame(
                {"observableId": ["obs_B"], "observableFormula": ["B"], "noiseFormula": [0.1]}
            )
        ),
        measurement_df=v1.get_measurement_df(
            pd.DataFrame(
                {"observableId": ["obs_B"], "simulationConditionId": ["c1"], "time": [1.0], "measurement": [0.5]}
            )
        ),
        parameter_df=v1.get_parameter_df(
            pd.DataFrame(
                {"parameterId": ["k1"], "parameterScale": ["lin"], "lowerBound": [1e-3],
                 "upperBound": [1e3], "nominalValue": [0.5], "estimate": [1]}
            )
        ),
    ).to_files_generic(path)


with tempfile.TemporaryDirectory() as tmp:
    tmp = Path(tmp)
    out = tmp / "v2"
    out.mkdir()
    petab1to2(write_v1(tmp / "v1_a", k1=0.5), out)
    petab1to2(write_v1(tmp / "v1_b", k1=0.7), out)
    problem = Problem.from_yaml(out / "problem.yaml")
    print(problem.model.sbml_model.getParameter("k1").getValue())
    # 0.5, expected 0.7

With reparametrize="priors" or "all" (#539), converting the same problem twice into the same directory reparametrizes twice: the model left from the first run already contains log10_<id>, so the second run adds log10_<id>_1.

The behaviour for a non-empty output directory is inconsistent: if the problem has a non-empty condition table, the second conversion fails, because an existing experiments.tsv raises a ValueError. Without one, it silently succeeds as above.

Possible fixes:

  1. Require the output directory to be empty or non-existent (and create it), and raise a ValueError otherwise. This makes the behaviour consistent, replaces the special case for experiments.tsv, and avoids leftover files from earlier runs. It rules out converting in place (output directory = input directory), which the samefile check seems meant to support, but converting in place overwrites the v1 files anyway.
  2. Only fix _copy_file, i.e., overwrite dest unless it's the same file as src. This is the minimal change, but leaves the inconsistency with experiments.tsv.

I'd go with 1.

🤖 Generated with Claude Code

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