Dezinger

Description

A plugin to apply median-based dezinger to PROJECTION (raw) data. The plugin works in a 3D mode (kernel_size x kernel_size x kernel_size).

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: "[]"

kernel_size:
    visibility: basic
    dtype: int
    description: Kernel size of the median filter.
    default: "3"

outlier_mu:
    visibility: basic
    dtype: float
    description: A threshold for detecting and removing outliers in data.              If set too small, dezinger acts like a median filter. The value of               the threshold is multiplied with a variance level in data.
    default: "0.1"

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 Dezinger

get_max_frames()

Setting nFrames to multiple with an upper limit of 4 frames.

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.

raw_data()

Return True if the output dataset should retain ImageKey/NoImageKey instances if they exist, i.e. keep the darks and flats NB. This is only available if out_dataset is created from an in_dataset

set_filter_padding(in_data, out_data)

Should be overridden to define how wide the frame should be for each input data set