sensospot_parser/tests/test_dynamic_range.py

import numpy
import pandas
import pytest


def test_check_if_xdr_ready_ok(exposure_df):
    from sensospot_data.columns import (
        SETTINGS_EXPOSURE_TIME,
        SETTINGS_EXPOSURE_CHANNEL,
    )
    from sensospot_data.dynamic_range import _check_if_xdr_ready

    exposure_df[SETTINGS_EXPOSURE_TIME] = 1
    exposure_df[SETTINGS_EXPOSURE_CHANNEL] = 2

    result = _check_if_xdr_ready(exposure_df)

    assert result is None


@pytest.mark.parametrize(["run"], [[0], [1], [2]])
def test_check_if_xdr_ready_raises_error_missing_column(exposure_df, run):
    from sensospot_data.columns import (
        SETTINGS_EXPOSURE_TIME,
        SETTINGS_EXPOSURE_CHANNEL,
    )
    from sensospot_data.dynamic_range import _check_if_xdr_ready

    columns = [SETTINGS_EXPOSURE_TIME, SETTINGS_EXPOSURE_CHANNEL, "X"]
    extra_col = columns[run]

    exposure_df[extra_col] = 1

    with pytest.raises(ValueError):
        _check_if_xdr_ready(exposure_df)


def test_check_if_xdr_ready_raises_error_mixed_channels(exposure_df):
    from sensospot_data.columns import (
        META_DATA_EXPOSURE_ID,
        SETTINGS_EXPOSURE_TIME,
        SETTINGS_EXPOSURE_CHANNEL,
    )
    from sensospot_data.dynamic_range import _check_if_xdr_ready

    exposure_df[SETTINGS_EXPOSURE_TIME] = 1
    exposure_df[SETTINGS_EXPOSURE_CHANNEL] = exposure_df[META_DATA_EXPOSURE_ID]

    with pytest.raises(ValueError):
        _check_if_xdr_ready(exposure_df)


def test_check_if_xdr_ready_raises_error_non_numeric_time(exposure_df):
    from sensospot_data.columns import (
        SETTINGS_EXPOSURE_TIME,
        SETTINGS_EXPOSURE_CHANNEL,
    )
    from sensospot_data.dynamic_range import _check_if_xdr_ready

    exposure_df[SETTINGS_EXPOSURE_TIME] = "X"
    exposure_df[SETTINGS_EXPOSURE_CHANNEL] = 2

    with pytest.raises(ValueError):
        _check_if_xdr_ready(exposure_df)


def test_check_if_xdr_ready_raises_error_on_nan(exposure_df):
    from sensospot_data.columns import (
        SETTINGS_EXPOSURE_TIME,
        SETTINGS_EXPOSURE_CHANNEL,
    )
    from sensospot_data.dynamic_range import _check_if_xdr_ready

    exposure_df[SETTINGS_EXPOSURE_TIME] = numpy.nan
    exposure_df[SETTINGS_EXPOSURE_CHANNEL] = 2

    with pytest.raises(ValueError):
        _check_if_xdr_ready(exposure_df)


def test_check_overflow_limit_defaults():
    from sensospot_data.columns import CALC_SPOT_OVERFLOW, RAW_DATA_SPOT_MEAN
    from sensospot_data.dynamic_range import _calc_overflow_info

    data_frame = pandas.DataFrame(data={RAW_DATA_SPOT_MEAN: [0.1, 0.5, 0.6]})

    result = _calc_overflow_info(data_frame)

    assert list(result[CALC_SPOT_OVERFLOW]) == [False, False, True]


def test_check_overflow_limit_custom_limit():
    from sensospot_data.columns import CALC_SPOT_OVERFLOW
    from sensospot_data.dynamic_range import _calc_overflow_info

    data_frame = pandas.DataFrame(data={"X": [4, 2, 3, 4]})

    result = _calc_overflow_info(data_frame, "X", 2)

    assert list(result[CALC_SPOT_OVERFLOW]) == [True, False, True, True]


def test_reduce_overflow_multiple_times(normalization_data_frame):
    from sensospot_data.dynamic_range import (
        PROBE_MULTI_INDEX,
        _reduce_overflow,
        _calc_overflow_info,
    )

    data_frame = _calc_overflow_info(normalization_data_frame, "Saturation", 1)
    result = _reduce_overflow(data_frame)

    sorted_results = result.sort_values(by=PROBE_MULTI_INDEX)

    assert list(sorted_results["Value"]) == [
        1,
        2,
        3,
        1,
        10,
        10,
        10,
        10,
        100,
        100,
        100,
        100,
    ]


def test_reduce_overflow_only_one_exposure_time(normalization_data_frame):
    from sensospot_data.dynamic_range import (
        SETTINGS_EXPOSURE_TIME,
        _reduce_overflow,
        _calc_overflow_info,
    )

    normalization_data_frame[SETTINGS_EXPOSURE_TIME] = 1

    data_frame = _calc_overflow_info(normalization_data_frame, "Saturation", 1)
    result = _reduce_overflow(data_frame)

    assert list(result["Value"]) == list(normalization_data_frame["Value"])


def test_blend(normalization_data_frame):
    from sensospot_data.dynamic_range import PROBE_MULTI_INDEX, blend

    result = blend(normalization_data_frame, "Saturation", 1)

    sorted_results = result.sort_values(by=PROBE_MULTI_INDEX)

    assert list(sorted_results["Value"]) == [
        1,
        2,
        3,
        1,
        10,
        10,
        10,
        10,
        100,
        100,
        100,
        100,
    ]


def test_blend_raises_error(normalization_data_frame):
    from sensospot_data.dynamic_range import SETTINGS_EXPOSURE_TIME, blend

    normalization_data_frame[SETTINGS_EXPOSURE_TIME] = "A"

    with pytest.raises(ValueError):
        blend(normalization_data_frame, "Saturation", 1)


def test_normalize_values_no_param(normalization_data_frame):
    from sensospot_data.columns import RAW_DATA_NORMALIZATION_MAP
    from sensospot_data.dynamic_range import (
        PROBE_MULTI_INDEX,
        blend,
        normalize_values,
    )

    reduced = blend(normalization_data_frame, "Saturation", 1)

    result = normalize_values(reduced)

    sorted_results = result.sort_values(by=PROBE_MULTI_INDEX)
    expected_values = [1, 4, 15, 1, 10, 10, 10, 10, 100, 100, 100, 100]

    for normalized_col in RAW_DATA_NORMALIZATION_MAP.values():
        assert list(sorted_results[normalized_col]) == expected_values


def test_normalize_values_custom_param(normalization_data_frame):
    from sensospot_data.columns import RAW_DATA_NORMALIZATION_MAP
    from sensospot_data.dynamic_range import (
        PROBE_MULTI_INDEX,
        blend,
        normalize_values,
    )

    reduced = blend(normalization_data_frame, "Saturation", 1)

    result = normalize_values(reduced, 100)

    sorted_results = result.sort_values(by=PROBE_MULTI_INDEX)
    expected_values = [2, 8, 30, 2, 20, 20, 20, 20, 200, 200, 200, 200]

    for normalized_col in RAW_DATA_NORMALIZATION_MAP.values():
        assert list(sorted_results[normalized_col]) == expected_values


def test_normalize_values_preset_param(normalization_data_frame):
    from sensospot_data.columns import (
        RAW_DATA_NORMALIZATION_MAP,
        SETTINGS_NORMALIZED_EXPOSURE_TIME,
    )
    from sensospot_data.dynamic_range import (
        PROBE_MULTI_INDEX,
        blend,
        normalize_values,
    )

    reduced = blend(normalization_data_frame, "Saturation", 1)
    reduced[SETTINGS_NORMALIZED_EXPOSURE_TIME] = 100

    result = normalize_values(reduced)

    sorted_results = result.sort_values(by=PROBE_MULTI_INDEX)
    expected_values = [2, 8, 30, 2, 20, 20, 20, 20, 200, 200, 200, 200]

    for normalized_col in RAW_DATA_NORMALIZATION_MAP.values():
        assert list(sorted_results[normalized_col]) == expected_values


def test_create_xdr(normalization_data_frame):
    from sensospot_data.columns import RAW_DATA_NORMALIZATION_MAP
    from sensospot_data.dynamic_range import PROBE_MULTI_INDEX, create_xdr

    result = create_xdr(normalization_data_frame, 100, "Saturation", 1)

    sorted_results = result.sort_values(by=PROBE_MULTI_INDEX)
    expected_values = [2, 8, 30, 2, 20, 20, 20, 20, 200, 200, 200, 200]

    for normalized_col in RAW_DATA_NORMALIZATION_MAP.values():
        assert list(sorted_results[normalized_col]) == expected_values