Parsing the numerical output from Sensovation SensoSpot image analysis.
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import numpy
from .columns import (
META_DATA_EXPOSURE_ID,
SETTINGS_EXPOSURE_TIME,
META_DATA_PARAMETERS_TIME,
SETTINGS_EXPOSURE_CHANNEL,
META_DATA_PARAMETERS_CHANNEL,
)
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 _set_exposure_data_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[META_DATA_PARAMETERS_CHANNEL].hasnans
or df[META_DATA_PARAMETERS_TIME].hasnans
):
raise ValueError("Exposure Map: measurement parameters incomplete")
df[SETTINGS_EXPOSURE_CHANNEL] = df[META_DATA_PARAMETERS_CHANNEL]
df[SETTINGS_EXPOSURE_TIME] = df[META_DATA_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 _set_exposure_data_from_parameters(data_frame)
existing = set(data_frame[META_DATA_EXPOSURE_ID].unique())
provided = set(exposure_map.keys())
if existing != provided:
raise ValueError(
f"Exposure Map differs from data frame: {provided} != {existing}"
)
data_frame[SETTINGS_EXPOSURE_CHANNEL] = numpy.nan
data_frame[SETTINGS_EXPOSURE_TIME] = numpy.nan
for exposure_id, exposure_info in exposure_map.items():
mask = data_frame[META_DATA_EXPOSURE_ID] == exposure_id
data_frame.loc[mask, SETTINGS_EXPOSURE_CHANNEL] = exposure_info.channel
data_frame.loc[mask, SETTINGS_EXPOSURE_TIME] = exposure_info.time
return data_frame