10-minute quickstart

10-minute quickstart#

This example downloads HTTomo’s small standard test dataset, validates a CPU pipeline and reconstructs the data with TomoPy. It does not require a GPU.

Prerequisites#

Install HTTomo by following CPU-only Conda environment. The example pipeline uses TomoPy and HTTomolib.

Download the data and pipeline#

Create an empty working directory and download the test dataset and CPU pipeline from the current HTTomo version:

$ mkdir httomo-quickstart
$ cd httomo-quickstart
$ curl -L -O \
    https://raw.githubusercontent.com/DiamondLightSource/httomo/main/tests/test_data/tomo_standard.nxs
$ curl -L -O \
    https://raw.githubusercontent.com/DiamondLightSource/httomo/main/docs/source/pipelines_full/tomopy_gridrec.yaml

The tomo_standard.nxs test dataset is approximately 9 MB. It contains 180 projections plus flat and dark fields, with detector frames of 128 by 160 pixels. tomopy_gridrec.yaml performs normalisation, automatic centre finding, gridrec reconstruction, rescaling and TIFF output.

Validate the pipeline#

Check both the pipeline syntax and the dataset paths before processing:

$ python -m httomo check tomopy_gridrec.yaml tomo_standard.nxs

A successful check finishes without validation errors.

Run the reconstruction#

Create the parent output directory, then run HTTomo:

$ mkdir output
$ python -m httomo run \
    tomo_standard.nxs tomopy_gridrec.yaml output

HTTomo creates a timestamped directory below output. The run reconstructs 128 slices of 160 by 160 pixels and writes them to the images8bit_tif subdirectory as TIFF files. The run directory also contains an intermediate HDF5 reconstruction, tomopy_gridrec.yaml, user.log and debug.log.

Open one of the TIFF files to confirm that the reconstruction completed. The centre reported in user.log should be close to 79.5 pixels.

Next steps#