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552 lines
15 KiB
552 lines
15 KiB
""" Stub file for testing the project """ |
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from pathlib import Path |
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import numpy |
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import pytest |
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EXAMPLE_DIR_WO_PARAMS = "mtp_wo_parameters" |
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EXAMPLE_DIR_WITH_PARAMS = "mtp_with_parameters" |
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@pytest.fixture |
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def example_dir(request): |
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root_dir = Path(request.config.rootdir) |
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yield root_dir / "example_data" |
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@pytest.fixture |
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def example_file(example_dir): |
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data_dir = example_dir / EXAMPLE_DIR_WO_PARAMS |
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yield data_dir / "160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv" |
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@pytest.fixture |
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def exposure_df(): |
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from pandas import DataFrame |
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yield DataFrame(data={"Exposure.Id": [1, 2, 3]}) |
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@pytest.fixture |
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def dir_for_caching(tmpdir, example_file): |
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import shutil |
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temp_path = Path(tmpdir) |
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dest = temp_path / example_file.name |
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shutil.copy(example_file, dest) |
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yield temp_path |
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@pytest.mark.parametrize( |
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"sub_dir, file_name", |
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[ |
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( |
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EXAMPLE_DIR_WO_PARAMS, |
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"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv", |
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), |
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( |
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EXAMPLE_DIR_WITH_PARAMS, |
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"160210_SG2-010-001_Regen_cy3100_1_A1_1.csv", |
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), |
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], |
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) |
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def test_parse_csv(example_dir, sub_dir, file_name): |
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from sensovation_data_parser.parser import _parse_csv |
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result = _parse_csv(example_dir / sub_dir / file_name) |
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columns = { |
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" ID ": numpy.int64, |
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"Pos.X": numpy.int64, |
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"Pos.Y": numpy.int64, |
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"Bkg.Mean": float, |
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"Spot.Mean": float, |
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"Bkg.Median": float, |
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"Spot.Median": float, |
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"Bkg.StdDev": float, |
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"Spot.StdDev": float, |
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"Bkg.Sum": numpy.int64, |
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"Spot.Sum": numpy.int64, |
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"Bkg.Area": numpy.int64, |
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"Spot.Area": numpy.int64, |
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"Spot.Sat. (%)": numpy.int64, |
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"Found": numpy.bool_, |
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"Pos.Nom.X": numpy.int64, |
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"Pos.Nom.Y": numpy.int64, |
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"Dia.": numpy.int64, |
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"Rect.": str, |
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"Contour": object, # ignore the type of contour |
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} |
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assert set(result.columns) == set(columns.keys()) |
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assert len(result[" ID "].unique()) == 100 |
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assert len(result) == 100 |
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for column, value_type in columns.items(): |
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assert isinstance(result[column][0], value_type) |
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def test_parse_csv_no_array(example_dir): |
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from sensovation_data_parser.parser import _parse_csv |
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result = _parse_csv(example_dir / "no_array_A1_1.csv") |
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assert len(result) == 1 |
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assert result[" ID "][0] == 0 |
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@pytest.mark.parametrize( |
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"input, expected", [("", "."), ("..,", "."), (".,,", ","), ("..,,", "."),] |
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) |
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def test_guess_decimal_separator_returns_correct_separator(input, expected): |
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from sensovation_data_parser.parser import _guess_decimal_separator |
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from io import StringIO |
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handle = StringIO(f"header\n{input}\n") |
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result = _guess_decimal_separator(handle) |
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assert result == expected |
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def test_guess_decimal_separator_rewinds_handle(): |
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from sensovation_data_parser.parser import _guess_decimal_separator |
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from io import StringIO |
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handle = StringIO(f"header\n{input}\n") |
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_guess_decimal_separator(handle) |
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assert next(handle) == "header\n" |
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def test_well_regex_ok(): |
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from sensovation_data_parser.parser import REGEX_WELL |
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result = REGEX_WELL.match("AbC123") |
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assert result["row"] == "AbC" |
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assert result["column"] == "123" |
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@pytest.mark.parametrize("input", ["", "A", "1", "1A", "-1", "A-"]) |
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def test_well_regex_no_match(input): |
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from sensovation_data_parser.parser import REGEX_WELL |
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result = REGEX_WELL.match(input) |
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assert result is None |
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@pytest.mark.parametrize( |
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"filename, expected", |
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[("A1_1.csv", ("A", 1, 1)), ("test/measurement_1_H12_2", ("H", 12, 2)),], |
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) |
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def test_extract_measurement_info_ok(filename, expected): |
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from sensovation_data_parser.parser import _extract_measurement_info |
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result = _extract_measurement_info(filename) |
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assert result == expected |
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@pytest.mark.parametrize("filename", ["wrong_exposure_A1_B", "no_well_XX_1"]) |
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def test_extract_measurement_info_raises_error(filename): |
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from sensovation_data_parser.parser import _extract_measurement_info |
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with pytest.raises(ValueError): |
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_extract_measurement_info(filename) |
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def test_cleanup_data_columns(): |
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from sensovation_data_parser.parser import _cleanup_data_columns |
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from pandas import DataFrame |
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columns = ["Rect.", "Contour", " ID ", "Found", "Dia."] |
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data = {col: [i] for i, col in enumerate(columns)} |
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data_frame = DataFrame(data=data) |
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result = _cleanup_data_columns(data_frame) |
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assert set(result.columns) == {"Pos.Id", "Spot.Found", "Spot.Diameter"} |
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assert result["Pos.Id"][0] == 2 |
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assert result["Spot.Found"][0] == 3 |
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assert result["Spot.Diameter"][0] == 4 |
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def test_parse_file(example_file): |
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from sensovation_data_parser.parser import parse_file |
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result = parse_file(example_file) |
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columns = { |
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"Pos.Id", |
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"Pos.X", |
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"Pos.Y", |
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"Bkg.Mean", |
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"Spot.Mean", |
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"Bkg.Median", |
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"Spot.Median", |
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"Bkg.StdDev", |
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"Spot.StdDev", |
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"Bkg.Sum", |
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"Spot.Sum", |
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"Bkg.Area", |
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"Spot.Area", |
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"Spot.Sat. (%)", |
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"Spot.Found", |
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"Pos.Nom.X", |
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"Pos.Nom.Y", |
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"Spot.Diameter", |
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"Field.Row", |
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"Field.Column", |
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"Exposure.Id", |
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} |
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assert set(result.columns) == columns |
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assert result["Field.Row"][0] == "A" |
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assert result["Field.Column"][0] == 1 |
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assert result["Exposure.Id"][0] == 1 |
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@pytest.mark.parametrize( |
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"file_list", |
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[ |
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[ |
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"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv", |
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"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv", |
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], |
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["160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv"], |
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], |
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) |
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def testparse_multiple_files_ok(example_dir, file_list): |
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from sensovation_data_parser.parser import parse_multiple_files |
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sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS |
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files = [sub_dir / file for file in file_list] |
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data_frame = parse_multiple_files(files) |
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print(data_frame["Exposure.Id"].unique()) |
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assert len(data_frame) == 100 * len(files) |
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assert len(data_frame["Exposure.Id"].unique()) == len(files) |
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def testparse_multiple_files_empty_file_list(): |
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from sensovation_data_parser.parser import parse_multiple_files |
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with pytest.raises(ValueError): |
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parse_multiple_files([]) |
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def testparse_multiple_files_empty_array(example_dir): |
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from sensovation_data_parser.parser import parse_multiple_files |
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files = [example_dir / "no_array_A1_1.csv"] |
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data_frame = parse_multiple_files(files) |
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print(data_frame["Exposure.Id"].unique()) |
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assert len(data_frame) == 1 |
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def test_list_csv_files(example_dir): |
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from sensovation_data_parser.parser import _list_csv_files |
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result = list(_list_csv_files(example_dir / EXAMPLE_DIR_WITH_PARAMS)) |
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assert len(result) == 36 * 3 |
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assert all(str(item).endswith(".csv") for item in result) |
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assert all(not item.stem.startswith(".") for item in result) |
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def test_parse_folder(example_dir): |
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from sensovation_data_parser.parser import parse_folder |
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data_frame = parse_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS) |
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assert len(data_frame) == 36 * 3 * 100 |
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assert len(data_frame["Field.Row"].unique()) == 3 |
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assert len(data_frame["Field.Column"].unique()) == 12 |
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assert len(data_frame["Exposure.Id"].unique()) == 3 |
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assert len(data_frame["Pos.Id"].unique()) == 100 |
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def test_sanity_check_ok(example_dir): |
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from sensovation_data_parser.parser import ( |
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_sanity_check, |
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parse_multiple_files, |
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) |
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sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS |
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file_list = [ |
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"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv", |
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"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv", |
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] |
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files = [sub_dir / file for file in file_list] |
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data_frame = parse_multiple_files(files) |
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result = _sanity_check(data_frame) |
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assert len(result) == len(data_frame) |
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def test_sanity_check_raises_value_error(example_dir): |
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from sensovation_data_parser.parser import ( |
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_sanity_check, |
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parse_multiple_files, |
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) |
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sub_dir = example_dir / EXAMPLE_DIR_WO_PARAMS |
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file_list = [ |
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"160218_SG2-013-001_Regen1_Cy3-100_1_A1_1.csv", |
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"160218_SG2-013-001_Regen1_Cy3-100_1_A1_2.csv", |
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] |
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files = [sub_dir / file for file in file_list] |
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data_frame = parse_multiple_files(files) |
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data_frame = data_frame.drop(data_frame.index[1]) |
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with pytest.raises(ValueError): |
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_sanity_check(data_frame) |
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def test_search_channel_info_file_ok(example_dir): |
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from sensovation_data_parser.parser import _search_channel_info_file |
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result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS) |
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assert result.suffix == ".svexp" |
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def test_search_channel_info_file_no_parameters_folder(example_dir): |
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from sensovation_data_parser.parser import _search_channel_info_file |
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result = _search_channel_info_file(example_dir / EXAMPLE_DIR_WO_PARAMS) |
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assert result is None |
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def test_search_channel_info_file_no_parameters_file(tmpdir): |
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from sensovation_data_parser.parser import _search_channel_info_file |
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params_dir = tmpdir / "Parameters" |
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params_dir.mkdir() |
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result = _search_channel_info_file(tmpdir) |
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assert result is None |
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def test_parse_channel_info(example_dir): |
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from sensovation_data_parser.parser import ( |
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_search_channel_info_file, |
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_parse_channel_info, |
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) |
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params = _search_channel_info_file(example_dir / EXAMPLE_DIR_WITH_PARAMS) |
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result = _parse_channel_info(params) |
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assert set(result.keys()) == {1, 2, 3} |
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assert result[1] == ("green", 100) |
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assert result[2] == ("red", 150) |
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assert result[3] == ("red", 15) |
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def test_get_valid_exposure_map_provided_ok(exposure_df): |
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from sensovation_data_parser.parser import ( |
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_get_valid_exposure_map, |
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ExposureInfo, |
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) |
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dummy_value = ExposureInfo(None, None) |
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exposure_map = {1: dummy_value, 2: dummy_value, 3: dummy_value} |
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result = _get_valid_exposure_map( |
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"/nonexistent", exposure_df, exposure_map=exposure_map |
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) |
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assert result == exposure_map |
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def test_get_valid_exposure_map_provided_not_ok(exposure_df): |
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from sensovation_data_parser.parser import _get_valid_exposure_map |
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exposure_map = {1: None, 2: None} |
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result = _get_valid_exposure_map( |
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"/nonexistent", exposure_df, exposure_map=exposure_map |
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) |
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assert set(result.keys()) == {1, 2, 3} |
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assert all(v == (None, None) for v in result.values()) |
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def test_get_valid_exposure_map_info_from_file_ok(example_dir, exposure_df): |
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from sensovation_data_parser.parser import _get_valid_exposure_map |
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result = _get_valid_exposure_map( |
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example_dir / EXAMPLE_DIR_WITH_PARAMS, exposure_df, exposure_map=None |
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) |
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assert set(result.keys()) == {1, 2, 3} |
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assert result[1] == ("green", 100) |
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assert result[2] == ("red", 150) |
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assert result[3] == ("red", 15) |
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def test_get_valid_exposure_map_info_from_file_not_ok( |
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example_dir, exposure_df |
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): |
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from sensovation_data_parser.parser import _get_valid_exposure_map |
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data_frame = exposure_df.drop(exposure_df.index[1]) |
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result = _get_valid_exposure_map( |
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example_dir / EXAMPLE_DIR_WITH_PARAMS, data_frame, exposure_map=None |
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) |
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assert set(result.keys()) == {1, 3} |
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assert all(v == (None, None) for v in result.values()) |
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def test_augment_exposure_map(exposure_df): |
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from sensovation_data_parser.parser import ( |
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_augment_exposure_map, |
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ExposureInfo, |
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) |
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exposure_map = { |
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1: ExposureInfo("red", 10), |
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2: ExposureInfo("green", 20), |
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3: ExposureInfo("blue", 50), |
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} |
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result = _augment_exposure_map(exposure_df, exposure_map) |
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assert result["Exposure.Id"][0] == 1 |
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assert result["Exposure.Channel"][0] == "red" |
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assert result["Exposure.Time"][0] == 10 |
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assert result["Exposure.Id"][1] == 2 |
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assert result["Exposure.Channel"][1] == "green" |
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assert result["Exposure.Time"][1] == 20 |
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assert result["Exposure.Id"][2] == 3 |
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assert result["Exposure.Channel"][2] == "blue" |
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assert result["Exposure.Time"][2] == 50 |
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def test_process_folder_with_exposure_map(example_dir): |
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from sensovation_data_parser.parser import _process_folder |
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result = _process_folder(example_dir / EXAMPLE_DIR_WITH_PARAMS) |
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assert len(result) == 36 * 100 * 3 |
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expected = [(1, "green", 100), (2, "red", 150), (3, "red", 15)] |
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for exposure_id, channel, time in expected: |
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mask = result["Exposure.Id"] == exposure_id |
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example_row = result.loc[mask].iloc[1] |
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assert example_row["Exposure.Channel"] == channel |
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assert example_row["Exposure.Time"] == time |
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def test_process_folder_without_exposure_map(example_dir): |
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from sensovation_data_parser.parser import _process_folder |
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from pandas import isnull |
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result = _process_folder(example_dir / EXAMPLE_DIR_WO_PARAMS) |
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assert len(result) == 96 * 100 * 3 |
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for exposure_id in range(1, 4): |
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mask = result["Exposure.Id"] == exposure_id |
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example_row = result.loc[mask].iloc[1] |
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print(type(example_row["Exposure.Channel"])) |
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assert isnull(example_row["Exposure.Channel"]) |
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assert isnull(example_row["Exposure.Time"]) |
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def test_process_folder_creates_cache(dir_for_caching): |
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from sensovation_data_parser.parser import ( |
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process_folder, |
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CACHE_FILE_NAME, |
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) |
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cache_path = dir_for_caching / CACHE_FILE_NAME |
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assert not cache_path.is_file() |
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result = process_folder(dir_for_caching) |
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assert len(result) == 100 |
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assert cache_path.is_file() |
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def test_process_folder_reads_from_cache(dir_for_caching, example_file): |
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from sensovation_data_parser.parser import process_folder |
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process_folder(dir_for_caching) |
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csv_file = dir_for_caching / example_file.name |
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csv_file.unlink() |
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result = process_folder(dir_for_caching) |
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assert len(result) == 100 |
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def test_process_folder_read_cache_fails_silently( |
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dir_for_caching, exposure_df |
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): |
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from sensovation_data_parser.parser import ( |
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process_folder, |
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CACHE_FILE_NAME, |
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) |
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cache_path = dir_for_caching / CACHE_FILE_NAME |
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exposure_df.to_hdf(cache_path, "unknown table") |
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result = process_folder(dir_for_caching) |
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assert result["Field.Row"][0] == "A" |
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def test_get_cache_table_name(): |
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from sensovation_data_parser.parser import _get_cache_table_name |
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from sensovation_data_parser import __version__ |
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result = _get_cache_table_name() |
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assert result.startswith("v") |
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assert result[1:] == __version__ |
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def test_process_folder_read_cache_no_cache_arg(dir_for_caching, exposure_df): |
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from sensovation_data_parser.parser import ( |
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process_folder, |
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_get_cache_table_name, |
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CACHE_FILE_NAME, |
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) |
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cache_path = dir_for_caching / CACHE_FILE_NAME |
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exposure_df.to_hdf(cache_path, _get_cache_table_name()) |
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result = process_folder(dir_for_caching, use_cache=False) |
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assert result["Field.Row"][0] == "A" |
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def test_process_folder_writes_cache(dir_for_caching): |
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from sensovation_data_parser.parser import ( |
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process_folder, |
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CACHE_FILE_NAME, |
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) |
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process_folder(dir_for_caching, use_cache=True) |
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cache_path = dir_for_caching / CACHE_FILE_NAME |
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assert cache_path.is_file() |
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def test_process_folder_writes_cache_no_cache_arg(dir_for_caching): |
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from sensovation_data_parser.parser import process_folder, CACHE_FILE_NAME |
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process_folder(dir_for_caching, use_cache=False) |
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cache_path = dir_for_caching / CACHE_FILE_NAME |
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assert not cache_path.is_file()
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