Ccpi Cgls Recon¶

Description¶

A Plugin to run the CCPi implementation of the CGLS reconstruction algorithm.

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: "[]"

centre_of_rotation:
    visibility: basic
    dtype: "[float, str, dict{int:float}]"
    description: Centre of rotation to use for the reconstruction.
    default: "0.0"
    example: It could be a fixed value, a dictionary of (sinogram number, value) pairs for a polynomial fit of degree 1, or a dataset name.

init_vol:
    visibility: hidden
    dtype: "[None,str]"
    description: Not an option.
    default: None
    example: "Type the name of the initialised dataset e.g. ['tomo']"

log:
    visibility: intermediate
    dtype: bool
    description:
        summary: Option to take the log of the data before reconstruction.
        verbose: Should be set to false if you use PaganinFilter
    default: "True"
    example: Set to True to take the log of the data before reconstruction.

preview:
    visibility: intermediate
    dtype: preview
    description: A slice list of required frames.
    default: "[]"

force_zero:
    visibility: intermediate
    dtype: "[list[float,float],list[None,None]]"
    description: Set any values in the reconstructed image outside of this range to zero.
    default: "['None', 'None']"
    example: "[0, 1]"

ratio:
    visibility: intermediate
    dtype: "[float, list[float, float]]"
    description: Ratio of the masks diameter in pixels to the smallest edge size along given axis. If a list of two floats is given, the second value is used to fill up the area outside the mask.
    default: "0.95"

log_func:
    visibility: advanced
    dtype: str
    description: Override the default log function with a numpy statement
    default: np.nan_to_num(-np.log(sino))

vol_shape:
    visibility: intermediate
    dtype: "[str, int]"
    description:
        summary: Override the size of the reconstruction volume with an integer value.
        verbose: When fixed, you get the dimension of the horizontal detector or you can specify any reconstruction size you like with an integer.
    default: fixed

n_iterations:
    visibility: basic
    dtype: int
    description: Number of iterations to perform.
    default: "5"

resolution:
    visibility: intermediate
    dtype: float
    description: Number of output voxels (res = n_pixels/n_voxels), set res > 1 for reduced resolution.
    default: "1"

n_frames:
    visibility: intermediate
    dtype: int
    description: This algorithm requires a multiple of 8 frames at a time for processing and this number may affect performance depending on your data size.
    options: "[8, 16, 24, 32]"
    default: "16"

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