sensospot_parser/tests/test_parser.py

""" Stub file for testing the project """

from pathlib import Path

import numpy
import pytest

EXAMPLE_DIR_WO_PARAMS = "mtp_wo_parameters"
EXAMPLE_DIR_WITH_PARAMS = "mtp_with_parameters"


@pytest.fixture
def example_dir(request):
    root_dir = Path(request.config.rootdir)
    yield root_dir / "example_data"


@pytest.fixture
def example_file(example_dir):
    data_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
    yield data_dir / "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv"


@pytest.fixture
def exposure_df():
    from pandas import DataFrame

    yield DataFrame(data={"Exposure.Id": [1, 2, 3]})


@pytest.fixture
def dir_for_caching(tmpdir, example_file):
    import shutil

    temp_path = Path(tmpdir)
    dest = temp_path / example_file.name
    shutil.copy(example_file, dest)
    yield temp_path


@pytest.mark.parametrize(
    "sub_dir, file_name",
    [
        (
            EXAMPLE_DIR_WO_PARAMS,
            "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
        ),
        (
            EXAMPLE_DIR_WITH_PARAMS,
            "160210_SG2-010-001_Regen_cy3100_1_A1_1.csv",
        ),
    ],
)
def test_parse_csv(example_dir, sub_dir, file_name):
    from sensovation_data_parser.parser import _parse_csv

    result = _parse_csv(example_dir / sub_dir / file_name)

    columns = {
        " ID ": numpy.int64,
        "Pos.X": numpy.int64,
        "Pos.Y": numpy.int64,
        "Bkg.Mean": float,
        "Spot.Mean": float,
        "Bkg.Median": float,
        "Spot.Median": float,
        "Bkg.StdDev": float,
        "Spot.StdDev": float,
        "Bkg.Sum": numpy.int64,
        "Spot.Sum": numpy.int64,
        "Bkg.Area": numpy.int64,
        "Spot.Area": numpy.int64,
        "Spot.Sat. (%)": numpy.int64,
        "Found": numpy.bool_,
        "Pos.Nom.X": numpy.int64,
        "Pos.Nom.Y": numpy.int64,
        "Dia.": numpy.int64,
        "Rect.": str,
        "Contour": object,  # ignore the type of contour
    }

    assert set(result.columns) == set(columns.keys())
    assert len(result[" ID "].unique()) == 100
    assert len(result) == 100
    for column, value_type in columns.items():
        assert isinstance(result[column][0], value_type)


def test_parse_csv_no_array(example_dir):
    from sensovation_data_parser.parser import _parse_csv

    result = _parse_csv(example_dir / "no_array_A1_1.csv")

    assert len(result) == 1
    assert result[" ID "][0] == 0


@pytest.mark.parametrize(
    "input, expected", [("", "."), ("..,", "."), (".,,", ","), ("..,,", "."),]
)
def test_guess_decimal_separator_returns_correct_separator(input, expected):
    from sensovation_data_parser.parser import _guess_decimal_separator
    from io import StringIO

    handle = StringIO(f"header\n{input}\n")
    result = _guess_decimal_separator(handle)

    assert result == expected


def test_guess_decimal_separator_rewinds_handle():
    from sensovation_data_parser.parser import _guess_decimal_separator
    from io import StringIO

    handle = StringIO(f"header\n{input}\n")
    _guess_decimal_separator(handle)

    assert next(handle) == "header\n"


def test_well_regex_ok():
    from sensovation_data_parser.parser import REGEX_WELL

    result = REGEX_WELL.match("AbC123")

    assert result["row"] == "AbC"
    assert result["column"] == "123"


@pytest.mark.parametrize("input", ["", "A", "1", "1A", "-1", "A-"])
def test_well_regex_no_match(input):
    from sensovation_data_parser.parser import REGEX_WELL

    result = REGEX_WELL.match(input)

    assert result is None


@pytest.mark.parametrize(
    "filename, expected",
    [("A1_1.csv", ("A", 1, 1)), ("test/measurement_1_H12_2", ("H", 12, 2)),],
)
def test_extract_measurement_info_ok(filename, expected):
    from sensovation_data_parser.parser import _extract_measurement_info

    result = _extract_measurement_info(filename)

    assert result == expected


@pytest.mark.parametrize("filename", ["wrong_exposure_A1_B", "no_well_XX_1"])
def test_extract_measurement_info_raises_error(filename):
    from sensovation_data_parser.parser import _extract_measurement_info

    with pytest.raises(ValueError):
        _extract_measurement_info(filename)


def test_cleanup_data_columns():
    from sensovation_data_parser.parser import _cleanup_data_columns
    from pandas import DataFrame

    columns = ["Rect.", "Contour", " ID ", "Found", "Dia."]
    data = {col: [i] for i, col in enumerate(columns)}
    data_frame = DataFrame(data=data)

    result = _cleanup_data_columns(data_frame)

    assert set(result.columns) == {"Pos.Id", "Spot.Found", "Spot.Diameter"}
    assert result["Pos.Id"][0] == 2
    assert result["Spot.Found"][0] == 3
    assert result["Spot.Diameter"][0] == 4


def test_parse_file(example_file):
    from sensovation_data_parser.parser import parse_file

    result = parse_file(example_file)

    columns = {
        "Pos.Id",
        "Pos.X",
        "Pos.Y",
        "Bkg.Mean",
        "Spot.Mean",
        "Bkg.Median",
        "Spot.Median",
        "Bkg.StdDev",
        "Spot.StdDev",
        "Bkg.Sum",
        "Spot.Sum",
        "Bkg.Area",
        "Spot.Area",
        "Spot.Sat. (%)",
        "Spot.Found",
        "Pos.Nom.X",
        "Pos.Nom.Y",
        "Spot.Diameter",
        "Field.Row",
        "Field.Column",
        "Exposure.Id",
    }

    assert set(result.columns) == columns
    assert result["Field.Row"][0] == "A"
    assert result["Field.Column"][0] == 1
    assert result["Exposure.Id"][0] == 1


@pytest.mark.parametrize(
    "file_list",
    [
        [
            "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
            "160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
        ],
        ["160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv"],
    ],
)
def testparse_multiple_files_ok(example_dir, file_list):
    from sensovation_data_parser.parser import parse_multiple_files

    sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
    files = [sub_dir / file for file in file_list]

    data_frame = parse_multiple_files(files)
    print(data_frame["Exposure.Id"].unique())

    assert len(data_frame) == 100 * len(files)
    assert len(data_frame["Exposure.Id"].unique()) == len(files)


def testparse_multiple_files_empty_file_list():
    from sensovation_data_parser.parser import parse_multiple_files

    with pytest.raises(ValueError):
        parse_multiple_files([])


def testparse_multiple_files_empty_array(example_dir):
    from sensovation_data_parser.parser import parse_multiple_files

    files = [example_dir / "no_array_A1_1.csv"]

    data_frame = parse_multiple_files(files)
    print(data_frame["Exposure.Id"].unique())

    assert len(data_frame) == 1


def test_list_csv_files(example_dir):
    from sensovation_data_parser.parser import _list_csv_files

    result = list(_list_csv_files(example_dir / EXAMPLE_DIR_WITH_PARAMS))

    assert len(result) == 36 * 3
    assert all(str(item).endswith(".csv") for item in result)
    assert all(not item.stem.startswith(".") for item in result)


def test_parse_folder(example_dir):
    from sensovation_data_parser.parser import parse_folder

    data_frame = parse_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS)

    assert len(data_frame) == 36 * 3 * 100
    assert len(data_frame["Field.Row"].unique()) == 3
    assert len(data_frame["Field.Column"].unique()) == 12
    assert len(data_frame["Exposure.Id"].unique()) == 3
    assert len(data_frame["Pos.Id"].unique()) == 100


def test_sanity_check_ok(example_dir):
    from sensovation_data_parser.parser import (
        _sanity_check,
        parse_multiple_files,
    )

    sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
    file_list = [
        "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
        "160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
    ]
    files = [sub_dir / file for file in file_list]
    data_frame = parse_multiple_files(files)

    result = _sanity_check(data_frame)

    assert len(result) == len(data_frame)


def test_sanity_check_raises_value_error(example_dir):
    from sensovation_data_parser.parser import (
        _sanity_check,
        parse_multiple_files,
    )

    sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS
    file_list = [
        "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv",
        "160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv",
    ]
    files = [sub_dir / file for file in file_list]
    data_frame = parse_multiple_files(files)
    data_frame = data_frame.drop(data_frame.index[1])

    with pytest.raises(ValueError):
        _sanity_check(data_frame)


def test_search_channel_info_file_ok(example_dir):
    from sensovation_data_parser.parser import _search_channel_info_file

    result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS)

    assert result.suffix == ".svexp"


def test_search_channel_info_file_no_parameters_folder(example_dir):
    from sensovation_data_parser.parser import _search_channel_info_file

    result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WO_PARAMS)

    assert result is None


def test_search_channel_info_file_no_parameters_file(tmpdir):
    from sensovation_data_parser.parser import _search_channel_info_file

    params_dir = tmpdir / "Parameters"
    params_dir.mkdir()

    result = _search_channel_info_file(tmpdir)

    assert result is None


def test_parse_channel_info(example_dir):
    from sensovation_data_parser.parser import (
        _search_channel_info_file,
        _parse_channel_info,
    )

    params = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS)
    result = _parse_channel_info(params)

    assert set(result.keys()) == {1, 2, 3}
    assert result[1] == ("green", 100)
    assert result[2] == ("red", 150)
    assert result[3] == ("red", 15)


def test_get_valid_exposure_map_provided_ok(exposure_df):
    from sensovation_data_parser.parser import (
        _get_valid_exposure_map,
        ExposureInfo,
    )

    dummy_value = ExposureInfo(None, None)
    exposure_map = {1: dummy_value, 2: dummy_value, 3: dummy_value}

    result = _get_valid_exposure_map(
        "/nonexistent", exposure_df, exposure_map=exposure_map
    )

    assert result == exposure_map


def test_get_valid_exposure_map_provided_not_ok(exposure_df):
    from sensovation_data_parser.parser import _get_valid_exposure_map

    exposure_map = {1: None, 2: None}

    result = _get_valid_exposure_map(
        "/nonexistent", exposure_df, exposure_map=exposure_map
    )

    assert set(result.keys()) == {1, 2, 3}
    assert all(v == (None, None) for v in result.values())


def test_get_valid_exposure_map_info_from_file_ok(example_dir, exposure_df):
    from sensovation_data_parser.parser import _get_valid_exposure_map

    result = _get_valid_exposure_map(
        example_dir / EXAMPLE_DIR_WITH_PARAMS, exposure_df, exposure_map=None
    )

    assert set(result.keys()) == {1, 2, 3}
    assert result[1] == ("green", 100)
    assert result[2] == ("red", 150)
    assert result[3] == ("red", 15)


def test_get_valid_exposure_map_info_from_file_not_ok(
    example_dir, exposure_df
):
    from sensovation_data_parser.parser import _get_valid_exposure_map

    data_frame = exposure_df.drop(exposure_df.index[1])

    result = _get_valid_exposure_map(
        example_dir / EXAMPLE_DIR_WITH_PARAMS, data_frame, exposure_map=None
    )

    assert set(result.keys()) == {1, 3}
    assert all(v == (None, None) for v in result.values())


def test_augment_exposure_map(exposure_df):
    from sensovation_data_parser.parser import (
        _augment_exposure_map,
        ExposureInfo,
    )

    exposure_map = {
        1: ExposureInfo("red", 10),
        2: ExposureInfo("green", 20),
        3: ExposureInfo("blue", 50),
    }

    result = _augment_exposure_map(exposure_df, exposure_map)

    assert result["Exposure.Id"][0] == 1
    assert result["Exposure.Channel"][0] == "red"
    assert result["Exposure.Time"][0] == 10
    assert result["Exposure.Id"][1] == 2
    assert result["Exposure.Channel"][1] == "green"
    assert result["Exposure.Time"][1] == 20
    assert result["Exposure.Id"][2] == 3
    assert result["Exposure.Channel"][2] == "blue"
    assert result["Exposure.Time"][2] == 50


def test_process_folder_with_exposure_map(example_dir):
    from sensovation_data_parser.parser import _process_folder

    result = _process_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS)

    assert len(result) == 36 * 100 * 3

    expected = [(1, "green", 100), (2, "red", 150), (3, "red", 15)]
    for exposure_id, channel, time in expected:
        mask = result["Exposure.Id"] == exposure_id
        example_row = result.loc[mask].iloc[1]
        assert example_row["Exposure.Channel"] == channel
        assert example_row["Exposure.Time"] == time


def test_process_folder_without_exposure_map(example_dir):
    from sensovation_data_parser.parser import _process_folder
    from pandas import isnull

    result = _process_folder(example_dir / EXAMPLE_DIR_WO_PARAMS)

    assert len(result) == 96 * 100 * 3

    for exposure_id in range(1, 4):
        mask = result["Exposure.Id"] == exposure_id
        example_row = result.loc[mask].iloc[1]
        print(type(example_row["Exposure.Channel"]))
        assert isnull(example_row["Exposure.Channel"])
        assert isnull(example_row["Exposure.Time"])


def test_process_folder_creates_cache(dir_for_caching):
    from sensovation_data_parser.parser import (
        process_folder,
        CACHE_FILE_NAME,
    )

    cache_path = dir_for_caching / CACHE_FILE_NAME
    assert not cache_path.is_file()

    result = process_folder(dir_for_caching)

    assert len(result) == 100
    assert cache_path.is_file()


def test_process_folder_reads_from_cache(dir_for_caching, example_file):
    from sensovation_data_parser.parser import process_folder

    process_folder(dir_for_caching)

    csv_file = dir_for_caching / example_file.name
    csv_file.unlink()

    result = process_folder(dir_for_caching)
    assert len(result) == 100


def test_process_folder_read_cache_fails_silently(
    dir_for_caching, exposure_df
):
    from sensovation_data_parser.parser import (
        process_folder,
        CACHE_FILE_NAME,
    )

    cache_path = dir_for_caching / CACHE_FILE_NAME
    exposure_df.to_hdf(cache_path, "unknown table")

    result = process_folder(dir_for_caching)

    assert result["Field.Row"][0] == "A"


def test_get_cache_table_name():
    from sensovation_data_parser.parser import _get_cache_table_name
    from sensovation_data_parser import __version__

    result = _get_cache_table_name()

    assert result.startswith("v")
    assert result[1:] == __version__


def test_process_folder_read_cache_no_cache_arg(dir_for_caching, exposure_df):
    from sensovation_data_parser.parser import (
        process_folder,
        _get_cache_table_name,
        CACHE_FILE_NAME,
    )

    cache_path = dir_for_caching / CACHE_FILE_NAME
    exposure_df.to_hdf(cache_path, _get_cache_table_name())

    result = process_folder(dir_for_caching, use_cache=False)

    assert result["Field.Row"][0] == "A"


def test_process_folder_writes_cache(dir_for_caching):
    from sensovation_data_parser.parser import (
        process_folder,
        CACHE_FILE_NAME,
    )

    process_folder(dir_for_caching, use_cache=True)

    cache_path = dir_for_caching / CACHE_FILE_NAME
    assert cache_path.is_file()


def test_process_folder_writes_cache_no_cache_arg(dir_for_caching):
    from sensovation_data_parser.parser import process_folder, CACHE_FILE_NAME

    process_folder(dir_for_caching, use_cache=False)

    cache_path = dir_for_caching / CACHE_FILE_NAME
    assert not cache_path.is_file()