How to use the benchexec.tablegenerator.columns.Column function in BenchExec

def test_column_init_no_error_on_default_scale(self):
        Column("memUsed", None, None, None, self.measure_type, "B")

def test_column_init_no_error_on_same_unit_without_scale(self):
        Column("memUsed", None, None, None, self.measure_type, "B", "B", None)

def setUp(self):
        self.max_dec_digits = 6
        self.sig_figures = 4
        self.measure_type = ColumnMeasureType(self.max_dec_digits)
        self.measure_column = Column(
            "CpuTime", None, self.sig_figures, None, self.measure_type, None, None, 1
        )
        self.default_optionals = (False, "html")

from benchexec.tablegenerator.columns import Column
import zipfile


# Process pool for parallel work.
# Some of our loops are CPU-bound (e.g., statistics calculations), thus we use
# processes, not threads.
# Fully initialized only in main() because we cannot do so in the worker processes.
parallel = util.DummyExecutor()

# Most important columns that should be shown first in tables (in the given order)
MAIN_COLUMNS = [
    Column("status"),
    Column("category"),
    Column("cputime"),
    Column("walltime"),
    Column("memory", unit="MB", source_unit="B"),
    Column(
        "memUsage", display_title="memory", unit="MB", source_unit="B"
    ),  # if old results are given
    Column("cpuenergy"),
]

NAME_START = "results"  # first part of filename of table

DEFAULT_OUTPUT_PATH = "results/"

TEMPLATE_FORMATS = ["html", "csv"]

_BYTE_FACTOR = 1000  # bytes in a kilobyte

UNIT_CONVERSION = {

Extract all columns mentioned in the result tag of a table definition file.
    """

    def handle_path(path):
        """Convert path from a path relative to table-definition file."""
        if not path or path.startswith("http://") or path.startswith("https://"):
            return path
        return os.path.join(os.path.dirname(table_definition_file), path)

    columns = []
    for c in xmltag.findall("column"):
        scale_factor = c.get("scaleFactor")
        display_unit = c.get("displayUnit")
        source_unit = c.get("sourceUnit")

        new_column = Column(
            c.get("title"),
            c.text,
            c.get("numberOfDigits"),
            handle_path(c.get("href")),
            None,
            display_unit,
            source_unit,
            scale_factor,
            c.get("relevantForDiff"),
            c.get("displayTitle"),
        )
        columns.append(new_column)

    return columns

for s in run_results
                for c in s.findall("column")
                if all_columns or c.get("hidden") != "true"
            }

            if not column_names:
                # completely empty results break stuff, add at least status column
                return [MAIN_COLUMNS[0]]

            # Put main columns first, then rest sorted alphabetically
            custom_columns = column_names.difference(
                column.title for column in MAIN_COLUMNS
            )
            return [
                column for column in MAIN_COLUMNS if column.title in column_names
            ] + [Column(title) for title in sorted(custom_columns)]

import benchexec.result as result
import benchexec.util
from benchexec.tablegenerator import htmltable, statistics, util
from benchexec.tablegenerator.columns import Column
import zipfile


# Process pool for parallel work.
# Some of our loops are CPU-bound (e.g., statistics calculations), thus we use
# processes, not threads.
# Fully initialized only in main() because we cannot do so in the worker processes.
parallel = util.DummyExecutor()

# Most important columns that should be shown first in tables (in the given order)
MAIN_COLUMNS = [
    Column("status"),
    Column("category"),
    Column("cputime"),
    Column("walltime"),
    Column("memory", unit="MB", source_unit="B"),
    Column(
        "memUsage", display_title="memory", unit="MB", source_unit="B"
    ),  # if old results are given
    Column("cpuenergy"),
]

NAME_START = "results"  # first part of filename of table

DEFAULT_OUTPUT_PATH = "results/"

TEMPLATE_FORMATS = ["html", "csv"]

import benchexec.util
from benchexec.tablegenerator import htmltable, statistics, util
from benchexec.tablegenerator.columns import Column
import zipfile


# Process pool for parallel work.
# Some of our loops are CPU-bound (e.g., statistics calculations), thus we use
# processes, not threads.
# Fully initialized only in main() because we cannot do so in the worker processes.
parallel = util.DummyExecutor()

# Most important columns that should be shown first in tables (in the given order)
MAIN_COLUMNS = [
    Column("status"),
    Column("category"),
    Column("cputime"),
    Column("walltime"),
    Column("memory", unit="MB", source_unit="B"),
    Column(
        "memUsage", display_title="memory", unit="MB", source_unit="B"
    ),  # if old results are given
    Column("cpuenergy"),
]

NAME_START = "results"  # first part of filename of table

DEFAULT_OUTPUT_PATH = "results/"

TEMPLATE_FORMATS = ["html", "csv"]

_BYTE_FACTOR = 1000  # bytes in a kilobyte

# Some of our loops are CPU-bound (e.g., statistics calculations), thus we use
# processes, not threads.
# Fully initialized only in main() because we cannot do so in the worker processes.
parallel = util.DummyExecutor()

# Most important columns that should be shown first in tables (in the given order)
MAIN_COLUMNS = [
    Column("status"),
    Column("category"),
    Column("cputime"),
    Column("walltime"),
    Column("memory", unit="MB", source_unit="B"),
    Column(
        "memUsage", display_title="memory", unit="MB", source_unit="B"
    ),  # if old results are given
    Column("cpuenergy"),
]

NAME_START = "results"  # first part of filename of table

DEFAULT_OUTPUT_PATH = "results/"

TEMPLATE_FORMATS = ["html", "csv"]

_BYTE_FACTOR = 1000  # bytes in a kilobyte

UNIT_CONVERSION = {
    "s": {"ms": 1000, "min": 1.0 / 60, "h": 1.0 / 3600},
    "B": {"kB": 1.0 / 10 ** 3, "MB": 1.0 / 10 ** 6, "GB": 1.0 / 10 ** 9},
    "J": {
        "kJ": 1.0 / 10 ** 3,
        "Ws": 1,