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108 lines
3.8 KiB
108 lines
3.8 KiB
from pandas.api.types import is_numeric_dtype |
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from .utils import split |
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from .columns import ( |
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RAW_DATA_POS_ID, |
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CALC_SPOT_OVERFLOW, |
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META_DATA_WELL_ROW, |
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RAW_DATA_SPOT_MEAN, |
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META_DATA_WELL_COLUMN, |
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SETTINGS_EXPOSURE_TIME, |
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SETTINGS_EXPOSURE_CHANNEL, |
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RAW_DATA_NORMALIZATION_MAP, |
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SETTINGS_NORMALIZED_EXPOSURE_TIME, |
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) |
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PROBE_MULTI_INDEX = [ |
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META_DATA_WELL_ROW, |
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META_DATA_WELL_COLUMN, |
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RAW_DATA_POS_ID, |
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] |
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def _check_if_xdr_ready(data_frame): |
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""" check if a data frame meets the constraints for xdr """ |
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required_columns = {SETTINGS_EXPOSURE_CHANNEL, SETTINGS_EXPOSURE_TIME} |
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if not required_columns.issubset(data_frame.columns): |
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raise ValueError("XDR: Apply an exposure map first") |
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if len(data_frame[SETTINGS_EXPOSURE_CHANNEL].unique()) != 1: |
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raise ValueError("XDR: Mixed Exposure Channels") |
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if not is_numeric_dtype(data_frame[SETTINGS_EXPOSURE_TIME]): |
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raise ValueError("XDR: Exposure time is not numerical") |
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if data_frame[SETTINGS_EXPOSURE_TIME].hasnans: |
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raise ValueError("XDR: Exposure time contains NaNs") |
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def _calc_overflow_info(data_frame, column=RAW_DATA_SPOT_MEAN, limit=0.5): |
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""" add overflow info, based on column and limit """ |
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data_frame.loc[:, CALC_SPOT_OVERFLOW] = data_frame[column] > limit |
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return data_frame |
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def _reduce_overflow(data_frame): |
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""" the heavy lifting for creating an extended dynamic range """ |
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split_frames = split(data_frame, SETTINGS_EXPOSURE_TIME) |
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# get the exposure times, longest first |
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exposure_times = sorted(split_frames.keys(), reverse=True) |
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max_time, *rest_times = exposure_times |
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result_frame = split_frames[max_time].set_index(PROBE_MULTI_INDEX) |
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for next_time in rest_times: |
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mask = result_frame[CALC_SPOT_OVERFLOW] == True # noqa: E712 |
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next_frame = split_frames[next_time].set_index(PROBE_MULTI_INDEX) |
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rf_index = set(result_frame.index) |
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nf_index = set(next_frame.index) |
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diff = rf_index - nf_index | nf_index - rf_index |
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if diff: |
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num = len(diff) |
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raise ValueError(f"XDR: Scan Data is incomplete, differs on {num} probes") |
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result_frame.loc[mask] = next_frame.loc[mask] |
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return result_frame.reset_index() |
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def blend(data_frame, column=RAW_DATA_SPOT_MEAN, limit=0.5): |
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""" creates an extended dynamic range, eliminating overflowing spots """ |
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_check_if_xdr_ready(data_frame) |
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if CALC_SPOT_OVERFLOW not in data_frame.columns: |
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data_frame = _calc_overflow_info(data_frame, column, limit) |
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return _reduce_overflow(data_frame) |
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def normalize_values(data_frame, normalized_time=None): |
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"""add exposure time normalized values to a data frame |
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will use the maximum exposure time, if none is provided |
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and the column SETTINGS_NORMALIZED_EXPOSURE_TIME was not |
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set before. |
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""" |
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if normalized_time: |
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data_frame[SETTINGS_NORMALIZED_EXPOSURE_TIME] = normalized_time |
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elif SETTINGS_NORMALIZED_EXPOSURE_TIME not in data_frame.columns: |
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normalized_time = data_frame[SETTINGS_EXPOSURE_TIME].max() |
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data_frame[SETTINGS_NORMALIZED_EXPOSURE_TIME] = normalized_time |
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for original_col, normalized_col in RAW_DATA_NORMALIZATION_MAP.items(): |
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data_frame[normalized_col] = ( |
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data_frame[original_col] / data_frame[SETTINGS_EXPOSURE_TIME] |
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) * data_frame[SETTINGS_NORMALIZED_EXPOSURE_TIME] |
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return data_frame |
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def create_xdr( |
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data_frame, |
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normalized_time=None, |
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column=RAW_DATA_SPOT_MEAN, |
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limit=0.5, |
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): |
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"""normalize measurement exposures |
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normalized_time: |
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if it is None, the max exposure time is used for normalization. |
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""" |
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data_frame = blend(data_frame, column, limit) |
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return normalize_values(data_frame, normalized_time)
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