Poly Background Estimator

Description

This plugin uses peakutils to find peaks in spectra. This is then metadata.

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: Create a list of the dataset(s).
    default: "['Peaks']"

n:
    visibility: basic
    dtype: int
    description: max number of polys.
    default: "2"

MaxIterations:
    visibility: intermediate
    dtype: int
    description: max number of iterations.
    default: "12"

weights:
    visibility: intermediate
    dtype: "[int, str, float, list]"
    description: weightings to apply.
    default: 1/data

pvalue:
    visibility: intermediate
    dtype: float
    description: Ratio of variance between successive poly iterations.
    default: "0.9"

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 PolyBackgroundEstimator

get_max_frames()

poly_background_estimator(xdata, ydata, n=2, weights=None, maxIterations=12, pvalue=0.9, fixed=False)

Background estimator based on orthogonal polynomials

Input: xdata,ydata (numpy arrays of same length) pvalue : ratio of variance in poly to poly value at which to stop. 0.9 default

Output:

background,polynomial weights, polynomials

S. Steenstrup J. Appl. Cryst. (1981). 14, 226–229 “A Simple Procedure for Fitting a Background to a Certain Class of Measured Spectra”

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.

setup()

This method is first to be called after the plugin has been created. It determines input/output datasets and plugin specific dataset information such as the pattern (e.g. sinogram/projection).

division_zero(x, y)