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<h2>Introduction</h2><a href="https://xia2.github.io/index.html" target="_blank" rel="noopener noreferrer">xia2</a> is an automation layer for macromolecular (and small-molecule) crystallography data reduction. Given a folder of raw diffraction images, it runs the full processing pipeline from indexing and integration to scaling and merging, orchestrating engines such as <a href="https://dials.github.io/" target="_blank" rel="noopener noreferrer">DIALS</a> and <a href="https://xds.mr.mpg.de/" target="_blank" rel="noopener noreferrer">XDS</a> and handling symmetry determination with POINTLESS/AIMLESS in <a href="https://www.ccp4.ac.uk/" target="_blank" rel="noopener noreferrer">CCP4</a>. The result is a consistent, reproducible pipeline that produces merged data (MTZ), logs, and a rich HTML report.This example <a href="https://app.dectris.cloud/sections/analysis/jobs/templates/public?id=0199c411-7994-70f2-b4fb-03411a61927a" target="_blank" rel="noopener noreferrer">job template</a> exemplifies how xia2 can be run as a <a href="https://help.dectris.cloud/en/article/jobs" target="_blank" rel="noopener noreferrer">job</a> on DECTRIS CLOUD with configurable input and machine performance types. The job template has been built with the <a href="01970982-ab14-7e71-a781-7bd380486ada" target="_blank" rel="noopener noreferrer">CCP4 &amp; XDS &amp; PyMOL command line</a> <a href="https://help.dectris.cloud/en/article/environments" target="_blank" rel="noopener noreferrer">environment</a>. You point it to an image directory, choose a pipeline (<code>dials</code>, <code>dials-aimless</code>, <code>3d</code>/<code>xds</code>, or <code>3dii</code>), and the template:<ul><li>builds the appropriate <code>xia2</code> command from your inputs (space group, unit cell, resolution limits, optional anomalous <code>atom=…</code>, and DIALS small-molecule mode),</li><li>runs the processing and collects the standard xia2 output,</li><li>generates summary plots (CC½, I/σ(I), completeness, multiplicity, etc.),</li><li>and records simple CPU/Memory usage over time.</li></ul>For XDS pipelines, an HDF5 plugin (<a href="https://github.com/dectris/neggia" target="_blank" rel="noopener noreferrer">dectris-neggia.so</a>) is provided as part of the environment and is included in the xia2 call.&nbsp;Note that this job template is based on an environment that is only available for academic users.<h2>Quick Start</h2>To run this job, valid input for the following variables are required to be defined:<ul><li>DC_PATH: Path to a folder within your experiment or project that contains your diffraction images. If you give relative image names, they’re resolved against this path. Example: <code>/dectris_data/DECTRIS/2025/23393/raw/ALS831_pilatus_1/data/</code></li></ul>Optionally, the following input variables can also be configured. If left with a value of -1, the input will be ignored within the job:<ul><li>DC_IMAGE: Makes it possible to specify the image input option for dials, for example to specify a specific dataset within a folder or a specific range of images to use. Up to five strings can be defined:&nbsp; Example: <code>data_J1_0001.cbf:1:200</code></li><li>DC_PIPELINE: Which backend to use. Allowed values: <code>dials</code> (DIALS + dials.scale), <code>dials-aimless</code> (DIALS + AIMLESS), <code>3d</code> (XDS), <code>3dii</code> (XDS all-frames peak search)</li><li>DC_SPACEGROUP: Specify a space group (otherwise let xia2/pointless decide). Example: <code>P212121</code></li><li>DC_UNIT_CELL: Specify a unit cell as six comma-separated values <code>a,b,c,alpha,beta,gamma</code>. Example: <code>78.3,94.1,101.2,90,90,90</code></li><li>DC_D_MIN: High-resolution cutoff in Å. Example: <code>2.2</code></li><li>DC_D_MAX: Low-resolution cutoff in Å. Example: <code>50</code></li><li>DC_MON_INTERVAL: Resource monitor sampling interval in seconds. Default: <code>30</code> Example: <code>10</code></li><li>DC_ATOM: Option to separate anomalous pairs. Example: <code>X</code></li><li>DC_SMALL_MOLECULE: Enable small-molecule mode (DIALS pipelines only: <code>dials</code>, <code>dials-aimless</code>). Ignored for XDS pipelines (<code>3d</code>, <code>3dii</code>). Example: <code>true</code></li></ul><figure class="image"><img src="https://cdn-assets.productfruits.com/bef24243-6ad4-479b-857d-dd6167e38947"></figure><h2>Output</h2>A successfully completed job generates the following outputs:<figure class="image"><img src="https://cdn-assets.productfruits.com/1268a031-e9eb-4fcd-be32-8688803306c3"></figure><figure class="image"><img src="https://cdn-assets.productfruits.com/ae7f8eb1-89f7-40c5-ab2d-07ee4c7b0139"></figure><figure class="image"><img src="https://cdn-assets.productfruits.com/723dee2d-535c-4444-b721-bdb2dd05f0b9"></figure>At the top level of the output folder, the following plots and files are defined:&nbsp;<ul><li>01_cc_half_vs_resolution.png: CC½ and CC-anom vs resolution (Å), with p=0.01 critical curves overlaid..</li><li>02_i_over_sigI_vs_resolution.png: Mean I/σ(I) vs resolution (Å): average signal-to-noise per bin.</li><li>03_second_moments.png: Second moment vs resolution (Å).</li><li>04_wilson_intensity.png: Wilson intensity plot (mean intensity vs resolution).</li><li>05_completeness_vs_resolution.png: Completeness (%) vs resolution (Å).</li><li>06_multiplicity_vs_resolution.png: Multiplicity (redundancy; observations per unique) vs resolution (Å).</li><li>cpu_total_vs_time.png: Node-wide CPU utilization (%) over job runtime.</li><li>mem_used_pct_vs_time.png: Node-wide memory used (%) over job runtime</li><li>resource_usage.csv: File used for tracking the resource usage during the job runtime. The file contains the following columns: <code>timestamp</code> (epoch time), <code>node_cpu_pct</code>,<code>mem_used_mb</code>,<code>mem_free_mb</code>,<code>mem_used_pct</code>,<code>load1</code> (1-minute load average).</li><li>resource_usage_with_mins.csv: Same as above plus an extra column <code>mins_since_start</code>, calculating the run time in minutes.</li><li>xia2-citations.txt: Relevant citations when generating results using xia2.</li><li>xia2-summary.txt: Overall results summary.</li><li>xia2.html: Full interactive xia2 report.</li><li>xia2_command.txt: The final xia2 command constructed by the job, based on your given input parameters.</li></ul>Within the subdirectory <code>xia2_output</code>, the typical xia2 output files can be found, including the subdirectories <code>DEFAULT</code>, <code>DataFiles</code>, and <code>LogFiles</code>.<h2>Example input configurations</h2><h3>Giving only the data source as input</h3>A minimal configuration of the job template involves configuring just DC_PATH and leaving all other parameters at their default value: <img src="https://cdn-assets.productfruits.com/d46013c3-1eb2-4c89-ace7-94a346575079">resulting in the following xia2 command to be constructed and called within the job template:<pre><code class="language-plaintext">xia2 pipeline=3d /dectris_data/TOMJO/2025/55080/raw/data_1/ hdf5_plugin=/opt/xds/neggia.so</code></pre><h3>Using a subset of images&nbsp;</h3>Sometimes you may want to process only part of a dataset, for example the early frames to minimize radiation damage. In the example below, only the first 500 images are included in the analysis, by specifying <code>xrd_data_0001.img:1:5000</code> for the first entry of DC_IMAGE:<figure class="image"><img src="https://cdn-assets.productfruits.com/3f49c45e-ae99-45bf-82de-a6996731dba6"></figure>The configured string is appended to DC_PATH, resulting in the following xia2 command:<pre><code class="language-plaintext">xia2 pipeline=3d image=/dectris_data/TOMJO/2025/55080/raw/data_1/xrd_data_0001.img:1:500 hdf5_plugin=/opt/xds/neggia.so</code></pre><h3>Specifying the cell parameters and unit cell</h3>If you already know the crystal geometry (from a reference dataset or prior refinement), you can fix it for the run:<figure class="image"><img src="https://cdn-assets.productfruits.com/20f7f964-35df-4b94-8814-8f09d8ab3eda"></figure>resulting in the following xia2 command:<pre><code class="language-plaintext">xia2 pipeline=3d /dectris_data/TOMJO/2025/55080/raw/data_1/ xia2.settings.space_group=P1 unit_cell=5.14,5.14,5.14,90,90,90 hdf5_plugin=/opt/xds/neggia.so</code></pre><h2>Versions</h2><h3>Version 0</h3><ul><li>Initial version of the script, built with the version 2 of the <a href="01970982-ab14-7e71-a781-7bd380486ada" target="_blank" rel="noopener noreferrer">CCP4 &amp; XDS &amp; PyMOL command line</a> environment.</li></ul>

<figure class="media"><div data-oembed-url="https://www.youtube.com/watch?v=h4Tj_aT6fEc"><div style="position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;"><iframe src="https://www.youtube.com/embed/h4Tj_aT6fEc" style="position: absolute; width: 100%; height: 100%; top: 0; left: 0;" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe></div></div></figure><h2>Data and Software used:</h2><ul><li>Raw data from <a href="https://www.ebi.ac.uk/empiar/EMPIAR-10950/">EMPIAR-10950</a> (<a href="https://doi.org/10.1038/s41598-023-29396-0">Fujita et al., 2023</a>)</li><li><a href="https://relion.readthedocs.io/en/release-5.0/">RELION-5</a> and ModelAngelo (<a href="https://doi.org/10.1038/s41586-024-07215-4">Jamali et al., 2024</a>)</li><li><a href="https://cryosparc.com/">CryoSPARC</a> (<a href="https://doi.org/10.1038/nmeth.4169">Punjani et al., 2017</a>)</li><li><a href="https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/">Coot</a> (<a href="http://journals.iucr.org/d/issues/2010/04/00/ba5144/ba5144.pdf">Emsley et al., 2010</a>)</li></ul><h3>DECTRIS CLOUD environments</h3><ul><li>Globus Data Transfer v0</li><li>CryoSPARC v1</li><li>RELION-5.0 v0</li><li>CCP4 &amp; XDS &amp; PyMOL GUI v1</li></ul><h2>Data upload:</h2><ul><li>Create a new project and start a session using the Globus environment</li><li>Follow the instructions of the <a href="https://help.dectris.cloud/en/article/upload-via-globus">help page</a> to set up and then click on <code>Browse Globus</code> button on the <a href="https://www.ebi.ac.uk/empiar/EMPIAR-10950/" target="_blank" rel="noopener noreferrer">EMPIAR-10950</a> dataset page within the Session.</li><li>Download the dataset to <code>/dectris_data/path_to_your_project/raw/</code>.</li><li>After the download is completed, check if the files are present via the DECTRIS CLOUD data browser.</li></ul><h2>Processing steps:</h2><ul><li>Generate the gain reference from the raw movies by running the&nbsp;<code> Calculate Gain Reference</code> public job template on the raw folder of your project. This job does not require a GPU.</li><li>Create a Session using the <code>CryoSPARC</code> Environment. Make sure the node has at least 1 GPU.</li><li>Follow the standard CryoSPARC workflow. You can find a general tutorial <a href="https://guide.cryosparc.com/processing-data/get-started-with-cryosparc-introductory-tutorial" target="_blank" rel="noopener noreferrer">here</a>.</li><li>Download the final map and copy it to a <code>/dectris_data/path_to_your_project/</code> subfolder.</li><li>To build the atomic model automatically, run the <code>ModelAngelo</code> public job template using the final map as input. This job requires a GPU.</li><li>Create a new Session using the <code>CCP4 &amp; XDS &amp; PyMOL GUI</code> Environment, open a terminal and run <code>coot</code>, load your map and model to inspect the result.</li></ul>&nbsp;

<figure class="media"><div data-oembed-url="https://youtu.be/nEofKBfTk6Y"><div style="position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;"><iframe src="https://www.youtube.com/embed/nEofKBfTk6Y" style="position: absolute; width: 100%; height: 100%; top: 0; left: 0;" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe></div></div></figure><h2>Data and software used</h2><ul><li>Sinogram data from: Vamvakeros, A., Matras, D., Ashton, T. E., Dong, H., Bauer, D., Odarchenko, Y., Price, S. W. T., Butler, K. T., Gutowski, O., Dippel, A.-C., von Zimmermann, M., Darr, J., Jacques, S. D. M., &amp; Beale, A. M. (2021). Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries: Datasets [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5172980">https://doi.org/10.5281/zenodo.5172980</a></li><li>Paper based on example data: A. Vamvakeros, D. Matras, T. E. Ashton, A. A. Coelho, H. Dong, D. Bauer, Y. Odarchenko, S. W. T. Price, K. T. Butler, O. Gutowski, A. C. Dippel, M. v. Zimmerman, J. A. Darr, S. D. M. Jacques, A. M. Beale, Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries. Small Methods 2021, 5, 2100512. <a href="https://doi.org/10.1002/smtd.202100512">https://doi.org/10.1002/smtd.202100512</a></li><li><a href="https://imagej.net/software/fiji/" target="_blank" rel="noopener noreferrer">Fiji (ImageJ)</a>: Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. <a href="https://doi.org/10.1038/nmeth.2019" target="_blank" rel="noopener noreferrer">doi:10.1038/nmeth.2019</a></li><li><a href="https://ndtomo.readthedocs.io/en/master/" target="_blank" rel="noopener noreferrer">nDTomo</a>: Vamvakeros, A., Papoutsellis, E., Dong, H., Docherty, R., Beale, A.M., Cooper, S.J., Jacques, S.D.M.J., nDTomo: A Python-Based Software Suite for X-ray Chemical Imaging and Tomography, 2025, <a href="https://doi.org/10.26434/chemrxiv-2025-h5t2x">https://doi.org/10.26434/chemrxiv-2025-h5t2x</a></li><li><a href="https://advancedphotonsource.github.io/GSAS-II-tutorials/" target="_blank" rel="noopener noreferrer">GSAS-II</a> Toby, B. H., &amp; Von Dreele, R. B. (2013). “GSAS-II: the genesis of a modern open-source all purpose crystallography software package”. Journal of Applied Crystallography, 46(2), 544-549. <a href=" https://doi.org/10.1107/S0021889813003531" target="_blank" rel="noopener noreferrer">doi:10.1107/S0021889813003531</a></li></ul><h2>DECTRIS CLOUD environments</h2><ul><li>Fiji (ImageJ) v0</li><li><a href="https://help.dectris.cloud/en/article/ndtomo" target="_blank" rel="noopener noreferrer">nDTomo</a> v0</li><li>nDTomo Jupyter Notebook v0</li><li><a href="https://help.dectris.cloud/en/article/gsas-ii" target="_blank" rel="noopener noreferrer">GSAS-II</a> v1</li></ul><h2>Processing steps</h2><h3>1. Project creation and data upload</h3>Create a project by navigating to Data → My Projects → Add Project + and fill out the metadata:<figure class="image"><img src="https://cdn-assets.productfruits.com/cb8829b0-e149-498b-acc0-c62e9f3a0e5d"></figure>Navigate inside the project space and upload the following .h5 files from the <a href="https://zenodo.org/records/5172980" target="_blank" rel="noopener noreferrer">zenodo repository</a>:<figure class="image"><img src="https://cdn-assets.productfruits.com/b436b9ba-1eeb-43da-8118-7b6a31af0a5e"></figure><h3>2. Data visualization in ImageJ</h3>To look at the data with ImageJ, you can use the public Fiji (ImageJ) environment to start a session.To start a session, navigate to Analysis → Public Environments and search for Fiji. Click on the Fiji environment card and then START SESSION in the new panel that pops out from the side.&nbsp;On the following page, the Fiji environment will be preselected. Configure your session by choosing a name, local software disk (space needed for the installed software, not the data in the cloud, for Fiji 16 GB is enough), and the sponsor.&nbsp;Finally choose the performance in the right panel before clicking NEXT STEP. This brings you to a page where you can select your created project.<figure class="image"><img src="https://cdn-assets.productfruits.com/d9228c29-70bf-45dd-80da-6394e17037d6"></figure>Once inside the virtual machine, you can start ImageJ by going to the the Fiji.app folder in the home directory:<figure class="image"><img src="https://cdn-assets.productfruits.com/08f3c902-cce0-410a-8ace-cb1ab1c05c4c"></figure>When opening the files with the hdf5 reader plugin, you can find them by navigating to the Desktop and going along the <code>/dectris_data/</code> path.<h3>3. Preprocessing with nDTomo in a python environment</h3>To process the sinograms, you can start a session with the nDTomo Jupyter environment.<figure class="image"><img src="https://cdn-assets.productfruits.com/e54e4e44-8e04-4feb-8c45-a46241a33722"></figure>Within the session, you can start a new notebook and use the following code for processing the sinograms. Feel free to experiment with improving the preprocessing steps:<pre><code class="language-python">import argparse, pathlib, h5py, numpy as np, matplotlib
import matplotlib.pyplot as plt
from nDTomo.tomo.sinograms import (
 airrem, scalesinos, sinocomcor, sinocentering
)
from nDTomo.tomo.conv_tomo import filter_sinogram, backproject
from pathlib import Path</code></pre>Import your uploaded sinogram data from your project. Make sure to adjust the path to match the mounted file path of your project:<pre><code class="language-python">with h5py.File("/dectris_data/CAMLA/2025/82169/raw/Pristine.h5", "r") as f:
 raw = f["data"][:] 
 q = f["q"][:]
stack = np.moveaxis(raw, (0, 1, 2), (2, 1, 0)) # (x, θ, q)
theta = np.linspace(0, 180,400, endpoint=False)</code></pre>The <code>moveaxis</code> command is used to make sure that the data is arranged in the way that nDTomo expects.We can now perform the preprocessing steps:<pre><code class="language-python">q_idx = 500 # Choose an index along the stack to visualize when doing corrections
plt.imshow(stack[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'1. Raw Sinogram, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = airrem(stack, ofs=2, method="both", coli=0, colf=2)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'2. airrem, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = scalesinos(sn)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'3. scalesinos, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = sinocomcor(sn, interp=False)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'4. sinocomcor, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = sinocentering(sn, crsr=20, interp=True, scan=180, channels=None, pbar=False)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'5. sinocentering, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()</code></pre>Perform reconstruction of just a single slice:<pre><code class="language-python">sino = sn[:, :, q_idx]
sino_f = filter_sinogram(sino, filter_type="Ramp")
test = backproject(sino_f, theta)
plt.imshow(test)</code></pre>Once satisfied with the above test, you can loop over all q-indices and make a reconstruction for each:<pre><code class="language-python">nx, ntheta, nq = sn.shape 
vol = np.empty((nx, nx, nq), dtype="float32")

for j in range(nq):
 sino = sn[:, :, j] 
 sino_f = filter_sinogram(sino, filter_type="Ramp")
 vol[:, :, j] = backproject(sino_f, theta)
 if j % 100 == 0:
 print(f"reconstructed {j}/{nq}")</code></pre>Make sure to save your reconstruction results in a .h5 file, if you want to continue the analysis within a different environment. In this case, the .h5 file is saved to the work directory of my project. Make sure to adjust the path to your own personal project folder:<pre><code class="language-python">with h5py.File('/dectris_data/CAMLA/2025/82169/work/simple_rc_battery.h5', 'w') as hf:
 hf.create_dataset('q', data=q)
 hf.create_dataset('data', data=vol)</code></pre><h3>4. Starting the nDTomo GUI</h3>If you want to proceed the analysis using the nDTomo GUI, you can start up the nDTomo environment. This environment differ from the previous one by being a system environment, meaning that you within the virtual machine will have access to an Ubuntu system, where the nDTomo components are installed.<figure class="image"><img src="https://cdn-assets.productfruits.com/7ee39acb-fdde-4961-b1b4-edffa54e8bbc"></figure><h3>5. Configuring a job to run batch refinements with GSAS-II</h3>As a final step, we will perform a Rietveld refinement of every spectrum in the .h5 file.&nbsp;This can be done by making use of the public <a href="https://help.dectris.cloud/en/article/gsas-ii-batch-refinement-battery" target="_blank" rel="noopener noreferrer">GSAS-II batch refinement battery</a> job template created for this use case.&nbsp;To run the job, we need to provide a path to the reconstructed .h5 file from the previous step, as well as a path to a folder containing the .cif files for the phases we are refining (Al, Cu, Fe).Before running the job, we therefore upload the files <code>al.cif</code>, <code>cu.cif</code> and <code>fe.cif</code> to the work folder in our project. The GSAS-II refinement job will look for these specific files when starting the refinement.<figure class="image"><img src="https://cdn-assets.productfruits.com/f180d542-4615-4d49-8bf6-8cfa88e4944e"></figure>Once this is done, you can access and run the job template by going to Analysis → Public Job Templates and choosing the <a href="https://help.dectris.cloud/en/article/gsas-ii-batch-refinement-battery" target="_blank" rel="noopener noreferrer">GSAS-II batch refinement battery</a> job template:<figure class="image"><img src="https://cdn-assets.productfruits.com/b4e4d4b8-c9b1-47c2-a722-d0cfa4273224"></figure>After running it, you should see the following output:<figure class="image"><img src="https://cdn-assets.productfruits.com/522bac1a-b079-4d35-ba6f-bb8e5521b4cd"></figure>The job template attempts to parallelize the refinements, meaning that the running time should decrease with more powerful machine types. The above results were achieved with a Hypersonic node (64 CPUs) and took 33 minutes, while a test run with the Turbo node (32 CPUs) took 51 minutes.<h3>6. Getting access to the demonstration project</h3>If you want to be added to the example project for the video, so you do not have to run the full reconstruction yourself, you can contact us at <a href="mailto:support@dectris.cloud">support@dectris.cloud</a>

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