Fast DP: Fast Data Processsing with XDS
Fast DP: Fast Data Processsing with XDS
Fast DP is a small Python program which uses XDS, CCP4 & CCTBX to deliver data processing results very quickly: quite how quickly will depend on the operating environment. In essence, the first image in the sweep is passed to the program, its header read and then XDS used to index with a triclinic lattice using spots drawn from small wedges of data around the start, 45 degrees in and 90 degrees in (or as close as possible to this). Integration is then performed in parallel, either using multiple cores or multiple processors if the XDS forkintegrate script is appropriately configured. The data are then scaled with XDS, still in P1, before analysis with Pointless. Finally the analysis from Pointless and the global postrefinement results from the XDS CORRECT step are then used to select a pointgroup, after which the data are re-scaled with XDS in this pointgroup and merged with Aimless.
At Diamond Light Source, using an appropriately configured cluster with a parallel file store, this process typically takes up to two minutes for any number of images.
fast_dp -h Usage: fast_dp.py [options] Options: -h, --help show this help message and exit -b BEAM, --beam=BEAM Beam centre: x, y (mm) -a ATOM, --atom=ATOM Atom type (e.g. Se) -j NUMBER_OF_JOBS, --number-of-jobs=NUMBER_OF_JOBS Number of jobs for integration -k NUMBER_OF_CORES, --number-of-cores=NUMBER_OF_CORES Number of cores for integration -J MAXIMUM_NUMBER_OF_JOBS, --maximum-number-of-jobs=MAXIMUM_NUMBER_OF_JOBS Maximum number of jobs for integration -c CELL, --cell=CELL Cell constants for processing, needs spacegroup -s SPACEGROUP, --spacegroup=SPACEGROUP Spacegroup for scaling and merging -1 FIRST_IMAGE, --first-image=FIRST_IMAGE First image for processing -N LAST_IMAGE, --last-image=LAST_IMAGE Last image for processing -r RESOLUTION_HIGH, --resolution-high=RESOLUTION_HIGH High resolution limit -R RESOLUTION_LOW, --resolution-low=RESOLUTION_LOW Low resolution limit
Conventional usage, e.g. on laptop, would be e.g:
giving the following output on a 2011 Macbook Pro:
Fast_DP installed in: /Users/graeme/svn/fast_dp Starting image: /Users/graeme/data/i04-BAG-training/th_8_2_0001.cbf Number of jobs: 1 Number of cores: 0 Processing images: 1 -> 540 Phi range: 82.00 -> 163.00 Template: th_8_2_####.cbf Wavelength: 0.97625 Working in: /private/tmp/fdp All autoindexing results: Lattice a b c alpha beta gamma tP 57.80 57.80 150.00 90.00 90.00 90.00 oC 81.80 81.70 150.00 90.00 90.00 90.00 oP 57.80 57.80 150.00 90.00 90.00 90.00 mC 81.80 81.70 150.00 90.00 90.00 90.00 mP 57.80 57.80 150.00 90.00 90.00 90.00 aP 57.80 57.80 150.00 90.00 90.00 90.00 Mosaic spread: 0.04 < 0.06 < 0.07 Happy with sg# 89 57.80 57.80 150.00 90.00 90.00 90.00 -------------------------------------------------------------------------------- Low resolution 28.89 28.89 1.37 High resolution 1.34 5.99 1.34 Rmerge 0.062 0.024 0.420 I/sigma 13.40 44.70 1.60 Completeness 99.6 98.9 96.1 Multiplicity 5.3 5.0 2.8 Anom. Completeness 96.5 100.0 71.4 Anom. Multiplicity 2.6 3.1 1.2 Anom. Correlation 99.9 99.9 76.0 Nrefl 306284 3922 11217 Nunique 57886 786 4030 Mid-slope 1.007 dF/F 0.075 dI/sig(dI) 0.823 -------------------------------------------------------------------------------- Merging point group: P 4 2 2 Unit cell: 57.78 57.78 150.01 90.00 90.00 90.00 Processing took 00h 03m 59s (239 s) [306284 reflections] RPS: 1277.6
The main result is the file fast_dp.mtz containing the scaled and merged intensities, a log file from Aimless for plotting the merging statistics and the information above in fast_dp.log.
See also fast_rdp to rerun last steps to change choices.
Winter, G. & McAuley, K. E. “Automated data collection for macromolecular crystallography.” Methods 55, 81-93 (2011).
Please also cite XDS, CCTBX & CCP4:
fast_dp depends on:
If all of these are installed and configured no further work is needed. For parallel operation in integration a forkintegrate script is needed to send jobs to your queuing system.
You can install the latest release version of fast_dp from PyPI by loading your CCTBX environment and then running
libtbx.pip install fast_dp
and update an existing installation to a newer version with
libtbx.pip install --upgrade fast_dp
You will then have to run eg.
to make sure all command line programs are set up correctly.
Installation for developers
If you are a developer then you can run
instead. This will check out a development copy of fast_dp into the cctbx modules directory and then install that to the system. To update your development copy you will need to update the repository as usual and then run
libtbx.python setup.py develop
in the source directory.
With prejudice the style guide for fast_dp is consistent PEP8 as implemented by black https://black.readthedocs.io/en/stable/ - installation is close to trivial (pip3 install black) and run with no options i.e. in fast_dp directory
will do what is needed to return the formatting to the defaults so that the diffs show only the code diffs not any formatting differences. There is no intention to be heavy handed about this, but having a style guide helps developers who contribute as there is no doubt.
The XDS.INP files generated by fast_dp make the following assumptions:
- All scans are about a single axis, approximately parallel to the detector “fast” axis (multi-axis goniometers are fine provided the axis for the scan is fixed)
- The detector is not offset in two-theta i.e. the beam is approximately perpendicular to the detector face.
- Currently templates are included for Pilatus 2M & 6M, ADSC and Rayonix CCD detectors - modification to other detectors may be possible.
fast_dp is provided with no guarantee of support however “best effort” support will be provided on contacting firstname.lastname@example.org. Users may be asked to provide example data in the event of a bug report.
fast_dp was developed at Diamond Light Source with the specific purpose of providing feedback to users about the merging statistics of their data in the shortest possible time. Clearly, however, it is very much dependent on XDS and its intrinsic parallelisation as well as CCP4 and CCTBX to operate, and without these fast_dp could not exist.
Copyright 2014 Diamond Light Source
Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Make sure you have written up your changes in the HISTORY.rst file.
To prepare a new fast_dp release you need to install bump2version, for example by running
pip install bump2version
or using libtbx.pip in an CCTBX environment, followed by a libtbx.configure. Releases can then be made by:
# Assuming current version is 1.1.1 bumpversion major # 1.1.1 -> 2.0.0 # or bumpversion minor # 1.1.1 -> 1.2.0 # or bumpversion patch # 1.1.1 -> 1.1.2 git push git push origin v1.1.2 # or git push origin v1.2.0 # or git push origin v2.0.0
The release tag, once pushed to Github, will be picked up by Travis which will generate a new package and upload it directly to PyPI.
- bugfix for Python 3 error
- add license file to release
- fast_dp is no longer supported with DIALS 1.12 and older versions
- add support for DIALS 2.1+
- add support for Python 3.8
- add support for DIALS 2.0
- use correct number of cores for integration with forkxds
- Improved support for spacegroup names. (#41)
- Report beam centre correctly in ispyb.xml for multi-panel detectors.
- fast_dp and fast_rdp return with a non-zero exit code when processing fails.
- Catch case where diffraction strong to edge of detector.
- Write out correct r_meas value in the fast_dp.json file.
- fast_dp.json format has changed. Scaling statistics are now stored in a structured dictionary. (#28)
- removed XDS.INP templates; now calculated on demand using dxtbx models from DIALS, thus allowing support for all beamlines currently understood by DIALS
- First release on PyPI.
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size fast_dp-1.6.2-py2.py3-none-any.whl (41.0 kB)||File type Wheel||Python version py2.py3||Upload date||Hashes View|
|Filename, size fast_dp-1.6.2.tar.gz (39.4 kB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for fast_dp-1.6.2-py2.py3-none-any.whl