Time Based Plus Drift Correction

Description

Apply a time-based dark and flat field correction on data with an image drift using linear interpolation and template matching.

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"

template:
    visibility: basic
    description: Region on the detector used to track the drift
    dtype: "list[str]"
    default: "['100:200', '100:200']"

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 TimeBasedPlusDriftCorrection

calculate_flat_field(frame, data, frames, distance)

calculate_flat_field_drift(template)

calculate_shift(im1, im2, template)

fill_nans(im1, im2)

post_process()

This method is called after the process function in the pipeline framework as a post-processing step. All processes will have finished performing the main processing at this stage.

Parameters

exp (experiment class instance) – An experiment object, holding input and output datasets

pre_process()

This method is called immediately after base_pre_process().

set_template_params()