Min And Max Deprecated¶
Description¶
A plugin to calculate the min and max values of each slice (as determined by the pattern parameter)
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: The default names.
default: "['the_min', 'the_max']"
method:
visibility: basic
dtype: str
options: "['extrema', 'percentile']"
description: Method to find the global min and the global max.
default: percentile
p_range:
visibility: basic
dtype: "list[float,float]"
description: "Percentage range if use the 'percentile' method."
default: "[0.0, 100.0]"
pattern:
visibility: basic
dtype: str
description: How to slice the data.
default: VOLUME_XZ
smoothing:
visibility: intermediate
dtype: bool
description: Apply a smoothing filter or not.
default: "True"
masking:
visibility: intermediate
dtype: bool
description: Apply a circular mask or not.
default: "True"
ratio:
visibility: intermediate
dtype: "[None,float]"
description: Used to calculate the circular mask. If not provided, it is calculated using the center of rotation.
default: None
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
MinAndMaxDeprecated
[source] -
circle_mask
(width, ratio)[source]
-
nOutput_datasets
()[source] The number of datasets created by the plugin
- Returns
Number of output datasets
-
post_process
()[source] 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
()[source] This method is called immediately after base_pre_process().
-
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).
-