sensospot_parser/sensospot_data/normalisation.py

import numpy

from .columns import (
    COL_NAME_POS_ID,
    COL_NAME_WELL_ROW,
    COL_NAME_SPOT_MEAN,
    COL_NAME_EXPOSURE_ID,
    COL_NAME_WELL_COLUMN,
    COLUMN_NORMALIZATION,
    COL_NAME_EXPOSURE_TIME,
    COL_NAME_SPOT_OVERFLOW,
    COL_NAME_PARAMETERS_TIME,
    COL_NAME_EXPOSURE_CHANNEL,
    COL_NAME_PARAMETERS_CHANNEL,
    COL_NAME_NORMALIZED_EXPOSURE_TIME,
)


def _split_data_frame(data_frame, column):
    """ splits a data frame on unique column values """
    values = data_frame[column].unique()
    masks = {value: (data_frame[column] == value) for value in values}
    return {value: data_frame[mask] for value, mask in masks.items()}


def _infer_exposure_from_parameters(data_frame):
    """infer the exposures from measurement parameters

    will raise a ValueError if the parameters contain NaNs
    """
    df = data_frame  # shorthand for cleaner code

    if (
        df[COL_NAME_PARAMETERS_CHANNEL].hasnans
        or df[COL_NAME_PARAMETERS_TIME].hasnans
    ):
        raise ValueError("Exposure Map: measurement parameters incomplete")

    df[COL_NAME_EXPOSURE_CHANNEL] = df[COL_NAME_PARAMETERS_CHANNEL]
    df[COL_NAME_EXPOSURE_TIME] = df[COL_NAME_PARAMETERS_TIME]
    return df


def apply_exposure_map(data_frame, exposure_map=None):
    """applies the parameters of a exposure map to the data frame

    exposure map:
        keys: must be the same as the exposure ids,
        values: objects with at least time and channel attributes

    if the exposure map is None, the values from the optionally parsed
    measurement parameters are used.

    will raise an ValueError, if the provided exposure map does not map to the
    exposure ids.
    """

    if exposure_map is None:
        return _infer_exposure_from_parameters(data_frame)

    existing = set(data_frame[COL_NAME_EXPOSURE_ID].unique())
    provided = set(exposure_map.keys())
    if existing != provided:
        raise ValueError(
            f"Exposure Map differs from data frame: {provided} != {existing}"
        )

    data_frame[COL_NAME_EXPOSURE_CHANNEL] = numpy.nan
    data_frame[COL_NAME_EXPOSURE_TIME] = numpy.nan
    for exposure_id, exposure_info in exposure_map.items():
        mask = data_frame[COL_NAME_EXPOSURE_ID] == exposure_id
        data_frame.loc[mask, COL_NAME_EXPOSURE_CHANNEL] = exposure_info.channel
        data_frame.loc[mask, COL_NAME_EXPOSURE_TIME] = exposure_info.time
    return data_frame


def _check_overflow_limit(data_frame, column=COL_NAME_SPOT_MEAN, limit=0.5):
    """ add overflow info, based on column and limit """
    data_frame[COL_NAME_SPOT_OVERFLOW] = data_frame[column] > limit
    return data_frame


def reduce_overflow(data_frame, column=COL_NAME_SPOT_MEAN, limit=0.5):
    """ reduces the data set per channel, eliminating overflowing spots """
    data_frame = _check_overflow_limit(data_frame, column, limit)

    split_frames = _split_data_frame(data_frame, COL_NAME_EXPOSURE_CHANNEL)

    return {
        channel_id: _reduce_overflow_in_channel(channel_frame)
        for channel_id, channel_frame in split_frames.items()
    }


def _reduce_overflow_in_channel(channel_frame):
    """ does the heavy lifting for reduce_overflow """

    split_frames = _split_data_frame(channel_frame, COL_NAME_EXPOSURE_TIME)

    if len(split_frames) == 1:
        # shortcut, if there is only one exposure in the channel
        return channel_frame

    exposure_times = sorted(split_frames.keys(), reverse=True)
    max_time, *rest_times = exposure_times

    multi_index = [COL_NAME_WELL_ROW, COL_NAME_WELL_COLUMN, COL_NAME_POS_ID]
    result_frame = split_frames[max_time].set_index(multi_index)

    for next_time in rest_times:
        mask = result_frame[COL_NAME_SPOT_OVERFLOW] == True  # noqa: E712
        next_frame = split_frames[next_time].set_index(multi_index)
        result_frame.loc[mask] = next_frame.loc[mask]

    return result_frame.reset_index()


def _infer_normalization_map(split_data_frames):
    """ extract a time normalization map from split data frames """
    return {
        key: frame[COL_NAME_EXPOSURE_TIME].max()
        for key, frame in split_data_frames.items()
    }


def normalize_exposure_time(split_data_frames):
    """add time normalized values to the split data frames

    The max exposure time per channel is used for normalization.
    """
    normalization_map = _infer_normalization_map(split_data_frames)
    return {
        key: normalize_channel(frame, normalization_map[key])
        for key, frame in split_data_frames.items()
    }


def normalize_channel(channel_frame, normalized_time):
    """ add time normalized values to a channel data frames """
    channel_frame[COL_NAME_NORMALIZED_EXPOSURE_TIME] = normalized_time

    for original_col, normalized_col in COLUMN_NORMALIZATION.items():
        channel_frame[normalized_col] = (
            channel_frame[original_col] / channel_frame[COL_NAME_EXPOSURE_TIME]
        ) * channel_frame[COL_NAME_NORMALIZED_EXPOSURE_TIME]

    return channel_frame


def normalize_measurement(
    data_frame,
    exposure_map=None,
    overflow_column=COL_NAME_SPOT_MEAN,
    overflow_limit=0.5,
):
    """augment normalize the measurement exposures

    exposure map:
        keys: must be the same as the exposure ids,
        values: objects with at least time and channel attributes
    if the exposure map is None, the values from the optionally parsed
    measurement parameters are used.

    The max exposure time per channel is used for normalization.
    """

    exposure_data_frame = apply_exposure_map(data_frame, exposure_map)
    split_data_frames = reduce_overflow(
        exposure_data_frame, overflow_column, overflow_limit
    )
    return normalize_exposure_time(split_data_frames)