Configuration parameters#

This file lists all the current configuration parameters available in the configuration file.

dataset#

Input dataset to integrate: path to a HDF5-Nexus file.

location =

Entry in the HDF5 file, if applicable. Default is the first available entry.

hdf5_entry =

Which folders layout to use for parsing datasets and creating output files. Available are: ID15A, ID11

layout = ID15A

azimuthal integration#

Number of azimuthal bins.

n_pts = 2500

Radial unit. Can be r_mm, q_A^-1, 2th_deg

unit = r_mm

Detector name (ex. eiger2_cdte_4m), or path to the a detector file specification (ex. id15_pilatus.h5)

detector =

Force detector name if not present in the ‘detector’ file description

detector_name =

Path to the mask file

mask_file =

Path to the pyFAI calibration file

poni_file =

Error model for azimuthal integration. Can be poisson or None.

error_model = poisson

azimuthal ranges in the form (min, max, n_slices)

azimuthal_range = (-180., 180., 1)

Lower and upper range of the radial unit. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored.

radial_range =

Which azimuthal integration method to use.

ai_method = opencl

Polarization factor for PyFAI. Default is 1.0

polarization_factor = 1.0

Whether to correct solid angle. Put this parameter if you wish to correct solid angle.

correct_solid_angle = 0

Whether to additionally perform azimuthal integration on average of each image stack

average_xy = 0

Whether to perform pixel splitting.

pixel_splitting = 0

Which outliers removal method to use. Can be none (default), median, or sigma clip. NB: when using one of the latter two, neither azimuthal caking nor uncertainty estimation are possible, only one azimuthal slice is used.

trim_method =

Number of azimuthal bins for trimmed mean.

trim_n_pts =

Bounds for trim methods:

  • For median: percentiles in the form (cut_low, cut_high). Integration uses medfilt1d with only one azimuthal slice.

  • sigma clip: thresholds in the form (thresh_low, thresh_high).

trim_bounds =

computations distribution#

Number of workers to use

n_workers = 10

Number of CPU cores per workers to use

cores_per_worker = 10

Time limit for SLURM job duration

time = 00:30:00

Amount of memory per worker. Default is 10 GB.

memory_per_worker = 10GB

Name of the project to be displayed on SLURM

project_name = distributed-azimuthal-integration

Whether to automatically respawn SLURM resources once the walltime is reached.

auto_respawn = 1

If non-empty, connect to an existing dask-distributed cluster. It should be in the form ‘tcp://ip:port’

cluster_address =

Name of the SLURM partition (queue)

partition = nice

Path to the python executable. Useful if the compute nodes have a different environment from the front-end. Defaults to current python executable (so accounts for virtual environments)

python_exe =

TCP Port for the dask-distributed scheduler

port = 36087

TCP Port for the dashboard

dashboard_port = 36088

Number of images processed in a single pass by each integrator. By default (empty), each integrator determines the optimal number of images to load.

n_images_per_integrator =

If auto_respawn is set to True, this defines the number of times the resources can be respawned.

max_respawns = 100

Period (in seconds)

healthcheck_period = 10

Defines a python executable to use for each SLURM partition. Mind the semicolon (;) as a separator.

python_executables = nice='/scisoft/tomotools_env/integrator/ubuntu20.04/x86_64/latest/bin/python' ; p9gpu='/scisoft/tomotools_env/integrator/ubuntu20.04/ppc64le/latest/bin/python' ; gpu='/scisoft/tomotools_env/integrator/ubuntu20.04/x86_64/latest/bin/python'

output#

Path to the output file. If not provided, it will be in the same directory as the input dataset. NOTA: a directory with the same name (less the extension) will be created at the same level, for storing the actual integrated data.

location =

What to do if output already exists. Possible values are:

  • skip: go to next image of the output already exists (and has the same processing configuration)

  • overwrite: re-do the processing, overwrite the file

  • raise: raise an error and exit

existing_output = skip

live integration#

pipeline#

Level of verbosity of the processing. 0 = terse, 3 = much information.

verbosity = 2