Time Based Correction

Description

Apply a time-based dark and flat field correction to data.

Parameters

in_datasets:
    visibility: datasets
    dtype: "[list[],list[str]]"
    description:
        summary: A list of the dataset(s) to process.
        verbose: A list of strings, where each string gives the name of a dataset that was either specified by a loader plugin or created as output to a previous plugin.  The length of the list is the number of input datasets requested by the plugin.  If there is only one dataset and the list is left empty it will default to that dataset.
    default: "[]"

out_datasets:
    visibility: datasets
    dtype: "[list[],list[str]]"
    description:
        summary: A list of the dataset(s) to create.
        verbose: A list of strings, where each string is a name to be assigned to a dataset output by the plugin. If there is only one input dataset and one output dataset and the list is left empty, the output will take the name of the input dataset. The length of the list is the number of output datasets created by the plugin.
    default: "[]"

in_range:
    visibility: intermediate
    dtype: bool
    description: "Set corrected values to be in the range [0, 1]."
    default: "False"

Key

visibility: The visibility level of the parameter
dtype: The type of data
description: A short explanation of the parameter
default: The default value
options: A list of permitted values
dependency: The name of the parameter and value which this parameter depends upon
range: A guide for the range of the parameter

Citations

No citations

API

class TimeBasedCorrection(name='TimeBasedCorrection')

calc_average(data, key)

calculate_dark_field(frame, data, frames, distance)

calculate_flat_field(frame, data, frames, distance)

find_nearest_frames(idx_list, value)

Find the index of the two entries that ‘value’ lies between in ‘idx_list’ and calculate the distance between each of them.

get_max_frames()

in_range(data, flat)

pre_process()

This method is called immediately after base_pre_process().

process_frames(data)

This method is called after the plugin has been created by the pipeline framework and forms the main processing step

Parameters

data (list(np.array)) – A list of numpy arrays for each input dataset.