Strip Background¶
Description¶
1D background removal. PyMca magic function
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: A list of the dataset(s) to process.
default: "['in_datasets[0]', 'background']"
iterations:
visibility: basic
dtype: int
description: Number of iterations.
default: "100"
window:
visibility: basic
dtype: int
description: Half width of the rolling window.
default: "10"
SG_filter_iterations:
visibility: intermediate
dtype: int
description: How many iterations until smoothing occurs.
default: "5"
SG_width:
visibility: intermediate
dtype: int
description: Whats the savitzgy golay window.
default: "35"
SG_polyorder:
visibility: intermediate
dtype: int
description: Whats the savitzgy-golay poly order.
default: "5"
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
StripBackground
[source] -
get_max_frames
()[source]
-
nOutput_datasets
()[source] The number of datasets created by the plugin
- Returns
Number of output datasets
-
process_frames
(data)[source] 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
()[source] 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).
-