def METHOD_NAME():
    key = "patient_id"
    df = pd.read_csv(this_dir + "testdata.csv")
    df.set_index(key, inplace=True)
    featureset = fstore.FeatureSet("testdata")
    _infer_from_static_df(df, featureset, options=InferOptions.all())
    # print(featureset.to_yaml())
    # test entity infer
    assert len(featureset.spec.entities) == 1, "entity not properly inferred"
    assert (
        list(featureset.spec.entities.keys())[0] == key
    ), "entity key not properly inferred"
    assert (
        list(featureset.spec.entities.values())[0].value_type == "str"
    ), "entity type not properly inferred"
    # test infer features
    assert (
        featureset.spec.features.to_dict() == expected_schema
    ), "did not infer schema properly"
    preview = featureset.status.preview
    # by default preview should be 20 lines + 1 for headers
    assert len(preview) == 21, "unexpected num of preview lines"
    assert len(preview[0]) == df.shape[1] + len(
        df.index.names
    ), "unexpected num of header columns"
    assert len(preview[1]) == df.shape[1] + len(
        df.index.names
    ), "unexpected num of value columns"
    features = sorted(featureset.spec.features.keys())
    stats = sorted(featureset.status.stats.keys())
    stats.remove(key)
    assert features == stats, "didnt infer stats for all features"
    stat_columns = list(featureset.status.stats["movements"].keys())
    assert stat_columns == [
        "count",
        "mean",
        "std",
        "min",
        "25%",
        "50%",
        "75%",
        "max",
        "hist",
    ], "wrong stats result"