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Processing of Eiger data is different from processing of conventional data, because the frames are wrapped into HDF5 files (ending with .h5). However, with the NEGGIA plugin for XDS, processing is as straightforward as before.

General aspects

  1. The framecache of XDS uses memory to save on I/O; it saves a frame in RAM after reading it for the first time. By default, each XDS (or mcolspot/mintegrate) job stores NUMBER_OF_IMAGES_IN_CACHE=DELPHI/OSCILLATION_RANGE images in memory which corresponds to one DELPHI-sized batch of data. This requires (number of pixels)*(number of jobs)*4 Bytes per frame which amounts to 72 MB in case of the Eiger 16M when running with MAXIMUM_NUBER_OF_JOBS=1. (If DELPHI=20 and OSCILLATION_RANGE=0.05 your computer thus has to have at least 400*72MB = 29GB of memory for each job). If it has not, the fallback is to the old behaviour of reading each frame three times (instead of once). There is an upper limit (8GB) to the amount of memory that will be used by default; if the required memory is more than that, a message will be printed and the user must explicitly include a NUMBER_OF_IMAGES_IN_CACHE= line in XDS.INP.
  2. Dectris provides a library [1] for native reading of HDF5 files, which can be loaded into XDS at runtime using the LIB= keyword. With this library, no conversion to CBF or otherwise is necessary. It is therefore just as fast and efficient to read HDF5 files as any other file format.
  3. The XDS BUILT=20170215 has a problem with constructing the master filename, see Eiger#Troubleshooting.

A suitable XDS.INP may have been written by the data collection (beamline) software. Latest generate_XDS.INP (generate_XDS.INP xxx_master.h5) or the XDS_from_H5.py script can be used if XDS.INP is not available.


The number of pixels of the Eiger 16M is three times higher than that of the Pilatus 6M, but since the Eiger firmware update in November 2015, the ("bit shufflle LZ4") compression of the .h5 files containing data is better than that of CBF files, which mostly compensates for the increased number of pixels.

The size of the *master.h5 file from a Eiger 16M experiment at SLS X06SA is more than 300MB, no matter how many frames are collected. It is therefore advisable to compress (by ~75%) the *master.h5 files on-site, before transferring them home using disk or internet. A very fast (parallel) program is lbzip2 (available from the EPEL repository for RHEL clones). It is supposedly fully compatible with bzip2.

Update 2016-06-05 (Toine Schreurs): a HDF5 file may be compressed with h5repack, e.g. by h5repack -i <in.h5> -o <out.h5> -f GZIP=6 (6 is the default compression level of gzip). This should be a good way to reduce the size of master files while keeping them compatible with processing, but needs to be tested. Whether h5repack uses parallel gzip is not clear from the docs.

A benchmark

Any comparisons should be based on a common dataset. I downloaded from https://www.dectris.com/datasets.html their latest dataset ftp://dectris.com/EIGER_16M_Nov2015.tar.bz2 (900 frames) and processed it on a single unloaded CentOS7.2 64bit machine with dual Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz , HT enabled (showing 32 processors in /proc/cpuinfo), on a local XFS filesystem (all defaults), with four JOBs and 12 PROCESSORS (the XDS.INP that Dectris provides suggests 8 JOBs of 12 PROCESSORS, but I changed that).

On multi-socket machines, there are additional considerations having to do with their NUMA architecture - see Performance.

Xeon Phi (Knights Landing, KNL)

The benchmark was run on a single KNL7210 processor (256 cores) set to quadrant mode and using the MCDRAM as cache. The environment variable OMP_PROC_BIND was set to false, or KMP_AFFINITY set to none (if this is not done, the scheduler seems to put all threads on one core). XDS was compiled with the -xMIC-AVX512 option of ifort. These benchmarks were performed with "warm" operating system cache, which means that the first run of a given type didn't count because it had to read all data from disk.

Deviating from the Xeon benchmark setup, BACKGROUND_RANGE was set to a more realistic value of 1 50 (instead of 1 9).

Using the Dectris library that makes use of the LIB= option of XDS:

INIT:            elapsed wall-clock time       30.4 sec
COLSPOT:         elapsed wall-clock time       40.7 sec
INTEGRATE: total elapsed wall-clock time       52.9 sec

Now additionally running with numactl --preferred=1 xds_par after having modified the forkintegrate script such that it starts mintegrate_par with the same numactl parameters:

INIT.LP:         elapsed wall-clock time       29.8 sec
COLSPOT:         elapsed wall-clock time       40.0 sec
INTEGRATE: total elapsed wall-clock time       51.3 sec

This was running with a 8GB/8GB split (hybrid) MCDRAM. The same run, but with 8 JOBS and 32 PROCESSORS, takes

INIT.LP:         elapsed wall-clock time       25.3 sec
COLSPOT:         elapsed wall-clock time       40.1 sec
INTEGRATE: total elapsed wall-clock time       53.1 sec

Back to 16 JOBS and 16 PROCESSORS, but with MCDRAM in flat mode und numactl --preferred=1 xds_par (thus using all 16GB for arrays, and nothing for cache):

INIT.LP:         elapsed wall-clock time       29.5 sec
COLSPOT:         elapsed wall-clock time       38.6 sec
INTEGRATE: total elapsed wall-clock time       53.2 sec

Now setting the KNL to SNC4 mode, and the MCDRAM to cache (using it in flat mode is impractical because the --preferred argument takes only 1 argument; to determine the correct argument requires scripting):

INIT.LP:         elapsed wall-clock time       29.6 sec
COLSPOT.LP:      elapsed wall-clock time       37.8 sec 
INTEGRATE: total elapsed wall-clock time       49.6 sec

If the library is compiled with -mtune=knl, all times are about 1 second less.

Conclusions: since INIT benefits from more PROCESSORs, one could run XDS twice for fastest turnaround; the first run with JOBS=XYCORR INIT and a high number of processors (99 is maximum). The second run with JOB=COLSPOT IDXREF DEFPIX INTEGRATE CORRECT, and an optimized JOBS/PROCESSORS combination. The SNC4 mode is fastest in this example - to do better than the cache mode of the MCDRAM, one needs to adapt the forkcolspot and forkintegrate script- see Performance. Other examples (with more frames) confirmed that cache mode is best for quadrant and SNC4, and resulted in quadrant mode being superior to SNC4. To optimally use the latter, one needs to thoroughly understand and properly use the relevant environment variables, in particular KMP_AFFINITY and KMP_PLACE_THREADS.

For comparison, if these data are stored as CBFs, COLSPOT and INTEGRATE take 34.8 and 45.2 seconds, respectively, in SNC4 mode. However, with a cold cache (i.e. when data are read for the first time), the HDF5 files have an advantage because they are a factor 2.5 smaller, due to the better compression.


  • make sure that master.h5 and the corresponding data.h5 files remain together as collected, and don't rename the data.h5 files - they are referred to from master.h5. If you change the names of the data.h5 files or copy them somewhere else, that link is broken unless you fix master.h5.
  • the very latest XDS (BUILT=20170215) has a problem with reading Eiger data - the master filename is not correctly constructed. The workaround is to either use the previous BUILT of 20161205, or to place a symlink e.g. ln -s my_data_master.h5 my_data_000001.h5. The next BUILT will of course fix the problem.

XDS_from_H5.py script for generating XDS.INP given a master .h5 file

This script could be made executable and put into /usr/local/bin. It requires the ALBULA API to be installed. If you get the error message

ImportError: No module named numpy.core.multiarray

you should

yum -y install numpy

as root.

# -*- coding: utf-8 -*-

__author__ = "AndF"
__date__ = "2017/03/08"
__reviewer__ = ""
__version__ = "0.1.0"

import sys
# Path needs to be be set only if dectris.albula is not found
# i.e. if ALBULA was installed without "--python=</path/to/python_interpreter>"
# Uncomment below (and define correct path to ALBULA API)
# sys.path.insert(0,"/usr/local/dectris/albula/3.2/python")
   import dectris.albula as dec
except ImportError:
   print "\nThe DECTRIS ALBULA API could not be loaded."
   print "If you did not install ALBULA with \"--python=</path/to/python_interpreter>\","
   print "please modify the \'sys.path.insert\' line in the script to point"
   print "to the DECTRIS ALBULA API and uncomment the line."
   raise SystemExit
import os.path
import re

# This script was developed by Andreas Förster at DECTRIS based on work by Marcus Mueller.
# Please note that this is not an official DECTRIS product and neither endorsed nor supported by DECTRIS.
# Please report errors and problems to docandreas@gmail.com.

XDS_header_lines = """!*****************************************************************************
! XDS.INP template for ! %(family)s %(detector)s with %(sensor).2f mm thick silicon sensors.
!    Characters to the right of an exclamation mark are comments.
!    This file was autogenerated by XDS_from_H5.py (Oct 2015).
!    Please check default values before processing.
! For questions and comments please contact docandreas@gmail.com.

XDS_detector_lines = """
!====================== DETECTOR PARAMETERS ==================================
!LIB= /usr/local/lib64/dectris-neggia.so
 OVERLOAD= %(cutoff)i ! taken from HDF5 header item
! /entry/instrument/detector/detectorSpecific/countrate_correction_count_cutoff
 SENSOR_THICKNESS=%(sensor).2f ! [mm]
 QX=%(pixsize_x).3f  QY=%(pixsize_y).3f  ! [mm]

XDS_main_lines = """
 TRUSTED_REGION=0.0 1.41 !Relative radii limiting trusted detector region

 DIRECTION_OF_DETECTOR_Y-AXIS= 0.0 1.0 0.0 ! 0.0 cos(2theta) sin(2theta)

!====================== JOB CONTROL PARAMETERS ===============================

!Set maximum number of jobs and processors so that their products comes close
!to the number of CPUs of the machine.
 MAXIMUM_NUMBER_OF_JOBS=8  !Speeds up COLSPOT & INTEGRATE on multicore machine
 MAXIMUM_NUMBER_OF_PROCESSORS=4!<99;ignored by single cpu version of xds
!SECONDS=0   !Maximum number of seconds to wait until data image must appear
!TEST=1     !Test flag. 1,2 additional diagnostics and images

!====================== GEOMETRICAL PARAMETERS ===============================
!ORGX and ORGY are often close to the image center, i.e. ORGX=NX/2, ORGY=NY/2
 ORGX= %(orgx).1f  ORGY= %(orgy).1f    !Detector origin (pixels).  ORGX=NX/2; ORGY=NY/2
 DETECTOR_DISTANCE= %(dist).2f   ! [mm]

 ROTATION_AXIS= 1.0 0.0 0.0

! Optimal choice is 0.5*mosaicity (REFLECTING_RANGE_E.S.D.= mosaicity)
 OSCILLATION_RANGE=%(osc_range).5f            ! [deg] (>0)

 X-RAY_WAVELENGTH=%(wavelength).4f           ! [A]
 FRACTION_OF_POLARIZATION=0.99 !default=0.5 for unpolarized beam

!======================= CRYSTAL PARAMETERS =================================
 SPACE_GROUP_NUMBER=0   !0 for unknown crystals; cell constants are ignored.

! You may specify here the x,y,z components for the unit cell vectors if
! known from a previous run using the same crystal in the same orientation

!Optional reindexing transformation to apply on reflection indices
!REIDX=   0  0 -1  0  0 -1  0  0 -1  0  0  0


!REFERENCE_DATA_SET= CK.HKL   ! Name of a reference data set (optional)

!==================== SELECTION OF DATA IMAGES ==============================
!Generic file name and format (optional) of data images
 NAME_TEMPLATE_OF_DATA_FRAMES=%(name_template)s ! HDF5

XDS_tail_lines = """
!==================== DATA COLLECTION STRATEGY (XPLAN) ======================
!                       !!! Warning !!!
! If you processed your data for a crystal with unknown cell constants and
! space group symmetry, XPLAN will report the results for space group P1.

!used to define the angular origin about the rotation axis.
!Default:  STARTING_ANGLE=  0 at STARTING_FRAME=first data image

!RESOLUTION_SHELLS=10 6 5 4 3 2 1.5 1.3 1.2



!====================== INDEXING PARAMETERS =================================
!Never forget to check this, since the default 0 0 0 is almost always correct!
!INDEX_ORIGIN= 0 0 0          ! used by "IDXREF" to add an index offset

!Additional parameters for fine tuning that rarely need to be changed
 SEPMIN=4.0       ! default is 6 for other detectors
 CLUSTER_RADIUS=2 ! default is 3 for other detectors

!Decision constants for detection of lattice symmetry (IDXREF, CORRECT)
 MAX_CELL_AXIS_ERROR= 0.03 ! Maximum relative error in cell axes tolerated
 MAX_CELL_ANGLE_ERROR= 2.0 ! Maximum cell angle error tolerated

!Decision constants for detection of space group symmetry (CORRECT).
!Resolution range for accepting reflections for space group determination in
!the CORRECT step. It should cover a sufficient number of strong reflections.
 MIN_RFL_Rmeas= 50  ! Minimum #reflections needed for calculation of Rmeas
 MAX_FAC_Rmeas= 2.0 ! Sets an upper limit for acceptable Rmeas

!================= PARAMETERS CONTROLLING REFINEMENTS =======================

!================== CRITERIA FOR ACCEPTING REFLECTIONS ======================
                   !for excluding shaded parts of the detector.

 INCLUDE_RESOLUTION_RANGE=50.0 %(reso_range).1f !Angstroem; used by DEFPIX,INTEGRATE,CORRECT

!used by CORRECT to exclude ice-reflections
!EXCLUDE_RESOLUTION_RANGE= 3.93 3.87 !ice-ring at 3.897 Angstrom
!EXCLUDE_RESOLUTION_RANGE= 3.70 3.64 !ice-ring at 3.669 Angstrom
!EXCLUDE_RESOLUTION_RANGE= 3.47 3.41 !ice-ring at 3.441 Angstrom
!EXCLUDE_RESOLUTION_RANGE= 2.70 2.64 !ice-ring at 2.671 Angstrom
!EXCLUDE_RESOLUTION_RANGE= 2.28 2.22 !ice-ring at 2.249 Angstrom
!EXCLUDE_RESOLUTION_RANGE= 2.102 2.042 !ice-ring at 2.072 Angstrom - strong
!EXCLUDE_RESOLUTION_RANGE= 1.978 1.918 !ice-ring at 1.948 Angstrom - weak
!EXCLUDE_RESOLUTION_RANGE= 1.948 1.888 !ice-ring at 1.918 Angstrom - strong
!EXCLUDE_RESOLUTION_RANGE= 1.913 1.853 !ice-ring at 1.883 Angstrom - weak
!EXCLUDE_RESOLUTION_RANGE= 1.751 1.691 !ice-ring at 1.721 Angstrom - weak

!MINIMUM_ZETA=0.05 !Defines width of 'blind region' (XPLAN,INTEGRATE,CORRECT)

!WFAC1=1.0  !This controls the number of rejected MISFITS in CORRECT;
        !a larger value leads to fewer rejections.
!REJECT_ALIEN=20.0 ! Automatic rejection of very strong reflections

!============== INTEGRATION AND PEAK PROFILE PARAMETERS =====================
!Specification of the peak profile parameters below overrides the automatic
!determination from the images
!Suggested values are listed near the end of INTEGRATE.LP
!BEAM_DIVERGENCE=   0.80         !arctan(spot diameter/DETECTOR_DISTANCE)
!BEAM_DIVERGENCE_E.S.D.=   0.080 !half-width (Sigma) of BEAM_DIVERGENCE
!REFLECTING_RANGE=  0.780 !for crossing the Ewald sphere on shortest route
!REFLECTING_RANGE_E.S.D.=  0.113 !half-width (mosaicity) of REFLECTING_RANGE

!The next two values could be increased up to 21 for best profiles.

!DELPHI= 6.0!controls the number of reference profiles and scaling factors
!CUT=2.0    !defines the integration region for profile fitting
!MINPK=75.0 !minimum required percentage of observed reflection intensity

!MINIMUM_I/SIGMA=3.0 !minimum intensity/sigma required for scaling reflections
!NBATCH=-1  !controls the number of correction factors along image numbers
!REFLECTIONS/CORRECTION_FACTOR=50   !minimum #reflections/correction needed

!STRONG_PIXEL=3.0                              !used by: COLSPOT
!A 'strong' pixel to be included in a spot must exceed the background
!by more than the given multiple of standard deviations.


!SPOT_MAXIMUM-CENTROID=3.0                     !used by: COLSPOT

!This allows to suppress spurious isolated pixels from entering the
!spot list generated by "COLSPOT".

!NBX=3  NBY=3  !Define a rectangle of size (2*NBX+1)*(2*NBY+1)
!The variation of counts within the rectangle centered at each image pixel
!is used for distinguishing between background and spot pixels.

!BACKGROUND_PIXEL=6.0                          !used by: COLSPOT,INTEGRATE
!An image pixel does not belong to the background region if the local
!pixel variation exceeds the expected variation by the given number of
!standard deviations.

!SIGNAL_PIXEL=3.0                              !used by: INTEGRATE
!A pixel above the threshold contributes to the spot centroid


detector_families = {
    'pilatus' : {
        'nmodules' : {
            '12M': (5, 24),
            '6M' : (5, 12),
            '2M' : (3 ,8),
            '1M' : (2, 5),
            '300K-W': (3, 1),
            '300K' : (1 ,3),
            '200K' : (1 ,2),
            '100K' : (1, 1),
        'module' : {
            'size': (487, 195),
            'gap': (7, 17),
            'pixel_size': (0.172e-03, 0.172e-03),
            'nchips': (8, 2),
        'chip': {
            'size': (60, 97),
            'gap': (1, 1),
        'sizes' : {}, # will be populated with correct sizes
    'eiger' : {
        'nmodules' : {
            '1M': (1, 2),
            '4M': (2, 4),
            '9M': (3, 6),
            '16M': (4, 8),
        'module' : {
            'size': (1030, 514),
            'gap': (10, 37),
            'pixel_size': (0.075e-03, 0.075e-03),
            'nchips': (4, 2),
        'chip' : {
            'size' : (256, 256),
            'gap' : (2, 2),
        'sizes' : {}, # will be populated with correct sizes

# All interesting parameters
incident_wavelength = "/entry/instrument/beam/incident_wavelength"
software_version = "/entry/instrument/detector/detectorSpecific/software_version"
beam_center_x = "/entry/instrument/detector/beam_center_x"
beam_center_y = "/entry/instrument/detector/beam_center_y"
x_pixel_size = "/entry/instrument/detector/x_pixel_size"
y_pixel_size = "/entry/instrument/detector/y_pixel_size"
detector_distance = "/entry/instrument/detector/detector_distance"
sensor_thickness = "/entry/instrument/detector/sensor_thickness"
nimages = "/entry/instrument/detector/detectorSpecific/nimages"
description = "/entry/instrument/detector/description"
omega_range_average = "/entry/sample/goniometer/omega_range_average"
countrate_correction_count_cutoff = "/entry/instrument/detector/detectorSpecific/countrate_correction_count_cutoff"
resolution_cutoff = 'max resolution'

# The list below contains the parameters to be extracted from H5
parameters = [

def create_XDS_INP(parameters, file_name):
    lines = []
    description = parameters["/entry/instrument/detector/description"].split()
    family = description[1].lower()
    sensor = float(parameters["/entry/instrument/detector/sensor_thickness"]) * 1000.0
    FAMILY = family.upper()
    det_name = description[2]
    file_template = re.sub("master\.h5", "??????.h5", file_name)
    lines.append(XDS_header_lines % {
        'family': FAMILY,
        'detector': det_name,
        'sensor': sensor,})
    lines.append(XDS_detector_lines % {
        'family': FAMILY,
        'cutoff': int(float(parameters["/entry/instrument/detector/detectorSpecific/countrate_correction_count_cutoff"])),
        'sensor': sensor,
        'pixsize_x': float(parameters["/entry/instrument/detector/x_pixel_size"]) * 1000.0,
        'pixsize_y': float(parameters["/entry/instrument/detector/y_pixel_size"]) * 1000.0,})
    lines = lines + get_size_specific_lines(fam=family, det=det_name, n_excluded_edge_pixels=0)
    lines.append(XDS_main_lines % {
        'orgx': float(parameters["/entry/instrument/detector/beam_center_x"]),
        'orgy': float(parameters["/entry/instrument/detector/beam_center_y"]),
        'dist': float(parameters["/entry/instrument/detector/detector_distance"]) * 1000.0,
        'osc_range': float(parameters["/entry/sample/goniometer/omega_range_average"]),
        'wavelength': float(parameters["/entry/instrument/beam/incident_wavelength"]),
        'name_template': file_template,})
    first = 1
    last = int(parameters["/entry/instrument/detector/detectorSpecific/nimages"])
    para_images = int(full_parameters["/entry/instrument/detector/detectorSpecific/nimages"])
    rotation = float(full_parameters["/entry/sample/goniometer/omega_range_average"])
    lines.append("\n DATA_RANGE=%i %i\n" % (first, last))
    if (para_images * rotation <= 30):
        if (last > 100):
            bkg = 100
            bkg = last
        lines.append(" BACKGROUND_RANGE=%i %i  ! Numbers of first and last data image for background\n" % (first, bkg))
        lines.append("!Five degrees are sufficient\n")
        lines.append(" SPOT_RANGE= %i %i       ! Image range for finding spots\n" % (first, last))
        lines.append("!Use all images if this range is not sufficient\n")
    elif (para_images * rotation > 30):
        # split spot finding into three 10 degree segments
        bkg = first + int(5/rotation)
        end1   = first + int(10/rotation)
        start2 = first + int(last/2)
        end2   = first + int(last/2) + int(10/rotation)
        start3 = first + last - int(10/rotation) - 1
        end3   = first + last - 1
        lines.append(" BACKGROUND_RANGE=%i %i  ! Numbers of first and last data image for background\n" % (first, bkg))
        lines.append("!Five degrees are sufficient\n")
        lines.append(" SPOT_RANGE= %i %i       ! First image range for finding spots\n" % (first, end1))
        lines.append(" SPOT_RANGE= %i %i       ! Second image range for finding spots\n" % (start2, end2))
        lines.append(" SPOT_RANGE= %i %i       ! Third image range for finding spots\n" % (start3, end3))
        lines.append("!Use all images if three ranges are not sufficient\n")
    lines.append(XDS_tail_lines % {
        'reso_range': float(parameters["max resolution"]),})
    return lines

def get_size_specific_lines(fam, det, n_excluded_edge_pixels=0):
    param_lines = []
    gaps = calculate_gaps(
    param_lines.append(' NX= %4d  NY= %4d \n\n' % detector_families[fam]['sizes'][det])
    param_lines.append('!EXCLUSION OF VERTICAL DEAD AREAS OF THE '
        '%s %s DETECTOR \n' % (fam.upper(), det))
    module_edge_comment = ('!EXCLUDING %d ADDITIONAL PIXELS OF THE '
        'MODULE EDGES \n' % n_excluded_edge_pixels)
    if n_excluded_edge_pixels > 0:
    # offset is required because XDS.INP pixel values start with 1, not 0
    offset = 1
    for gap in gaps[0]:
        param_lines.append(' UNTRUSTED_RECTANGLE= %4d %4d   %4d %4d \n' % (
            gap[0] - 1 + offset - n_excluded_edge_pixels,
            gap[1] + 1 + offset + n_excluded_edge_pixels,
            detector_families[fam]['sizes'][det][1] + offset))
        '%s %s DETECTOR \n' % (fam.upper(), det))
    if n_excluded_edge_pixels > 0:
    for gap in gaps[1]:
        param_lines.append(' UNTRUSTED_RECTANGLE= %4d %4d   %4d %4d \n' % (
            detector_families[fam]['sizes'][det][0] + offset,
            gap[0] - 1 + offset - n_excluded_edge_pixels,
            gap[1] + 1 + offset + n_excluded_edge_pixels))
    return param_lines

def warning():
    return ('\nThis script extracts from a given HDF5 master file all metadata\n'
            'required to write XDS.INP.  The user is prompted for missing metadata.\n'
            'If there are errors in the metadata, XDS.INP will be incorrect.\n'
            'Please report shortcomings and errors to docandreas@gmail.com\n')

def help():
    return ('ERROR - You must specify exactly one HDF5 master file:\n'
            'python XDS_from_H5.py <name>_master.h5\n')

def version_check(version):
    if float(re.search('^\d+\.\d+', version).group(0)) > 1.2:
        return 1
        return 0

zero_values = [0, "0", 0.0, "0.0"]

def isFile(file_input):
    '''This function verifies that the file name entered by the user
    corresponds to a master.h5 file and attaches an extension if necessary.'''
    if os.path.isfile(file_input) and re.search("master\.h5\Z", file_input):
        return file_input
    elif os.path.isfile(file_input + ".h5") and re.search("master\Z", file_input):
        return(file_input + ".h5")
        return 0

def request_parameter(parameter):
    if (parameter == omega_range_average):
        return raw_input("Please enter the oscillation range in degrees.\n")
    elif (parameter == detector_distance):
        return raw_input("Please enter the detector distance in meters.\n")
    elif (parameter == incident_wavelength):
        return raw_input("Please enter the wavelength in Angstrom.\n")
    elif (parameter == beam_center_x):
        return raw_input("Please enter the x coordinate of the beam center in pixels.\n")
    elif (parameter == beam_center_y):
        return raw_input("Please enter the y coordinate of the beam center in pixels.\n")
    elif (parameter == x_pixel_size):
        return raw_input("Please enter the x coordinate of the pixel size.\n")
    elif (parameter == y_pixel_size):
        return raw_input("Please enter the y coordinate of the pixel size.\n")
    elif (parameter == sensor_thickness):
        return raw_input("Please enter the sensor thickness in meters.\n")
    elif (parameter == nimages):
        return raw_input("Please enter the number of images.\n")
    elif (parameter == description):
        print "Please enter the description of the detector, e.g."
        return raw_input("Dectris Eiger 4M\n")
    elif (parameter == countrate_correction_count_cutoff):
        return raw_input("Please enter the maximum trusted pixel value.\n")
    elif (parameter == resolution_cutoff):
        print "Please enter a resolution limit for processing."
        return raw_input("Enter '0' to let XDS decide.\n") or 0
        print "Unknown software version.  Please check."
        return 0

def calculate_gaps(det_size, mod_size, gap_size):
    Return list of tuples with first and last pixel in each detector gap.

    One list for each detector dimension (x and y).
    Input: total detector size in pixels
        size of a module in pixels
        size of a gap in pixels
    ndims = len(det_size)
    gaps = []
    for dim_index in range(ndims):
        module_start = 0
        while module_start < det_size[dim_index]:
            # First pixel on a module has index 0, Python and C style
            gap_start = module_start + mod_size[dim_index]
            module_start = gap_start + gap_size[dim_index]
            gap_end = module_start - 1
            if module_start < det_size[dim_index]:
                gaps[dim_index].append((gap_start, gap_end))
    return gaps

# Creates a dictionary of all keys and values in the NeXus tree
def iterate_children(node, nodeDict={}):
    """ iterate over the children of a neXus node """
    if node.type() == dec.DNeXusNode.GROUP:
        for kid in node.children():
            nodeDict = iterate_children(kid, nodeDict)
        nodeDict[node.path()] = node.value()
    return nodeDict

# Extracts values from HDF5 file according to parameters array
def get_params(hdf5_file):
    extracted = {}
    h5cont = dec.DImageSeries(hdf5_file)
    neXus_tree = h5cont.neXus()
    neXus_root = neXus_tree.root()
    neXus_string_tree = iterate_children(neXus_root)
    if (len(sys.argv) == 2):
        print "Extracting metadata from " + hdf5_file
        print "Please modify XDS.INP if these numbers are incorrect.\n"
    for i in parameters:
        if (neXus_string_tree.has_key(i)):
            extracted[i] = str(neXus_string_tree[i])
            extracted[i] = ""
    return extracted

def calculate_size(n_modules, mod_size, gap_size):
    n_gaps = [n - 1 for n in n_modules]
    size = []
    for nmod, ngap, nmodpix, ngappix in zip(n_modules, n_gaps, mod_size, gap_size):
        size.append(nmod * nmodpix + ngap * ngappix)
    return tuple(size)

# populate dicts with sizes of detectors in pixels
for family in detector_families.values():
    for model, n_modules in family['nmodules'].items():
        family['sizes'][model] = calculate_size(n_modules=n_modules,

if __name__ == "__main__":
    if len(sys.argv) == 2:
        # Make sure that XDS.INP does not already exist
        if os.path.isfile ("XDS.INP"):
            print "\nERROR: XDS.INP exists already.  Please rename and rerun script."
            # test whether argument 1 is HDF5 file.
            # attach ".h5" if necessary
            clean_file = isFile(sys.argv[1])
            if (clean_file):
                print warning()
                full_parameters = get_params(clean_file)
                for i, v in full_parameters.iteritems():
                    if (v in zero_values):
                        print i + " = " + str(v) + "   <== WARNING:  Should this really be 0?"
                        full_parameters[i] = request_parameter(i)
                        print i + " = " + str(full_parameters[i])
                    elif (v == "NaN") or (v == ""):
                        print i + " = " + v + "   <== ERROR:  undefined value."
                        full_parameters[i] = request_parameter(i)
                        print i + " = " + str(full_parameters[i])
                        print i + " = " + str(v)
                para_version = str(full_parameters[software_version])
                if version_check(para_version):
                    param_lines = create_XDS_INP(full_parameters, clean_file)
                    open("XDS.INP", 'w').writelines(param_lines)
                    print "\nFile XDS.INP was created successfully."
                    if (int(full_parameters["/entry/instrument/detector/detectorSpecific/nimages"]) == 1):
                        print "However, there's not much you can do with one image.\n"
                        print "Please verify its contents before processing data.\n"
                    print "\nThe HDF5 file was created with version %s of the detector firmware" % (para_version)
                    print "This script supports versions 1.5 and up."
                    print "\nFile XDS.INP was not created."
                    print "Please extract metadata with hdfview or h5dump.\n"
                print help()
    elif (len(sys.argv) == 3):
        # This assumes the second argument is the rotation range
        # The script will run non-interactively
        # The master.h5 must be specified with its full name
        # An existing XDS.INP will be overwritten
        full_parameters = get_params(sys.argv[1])
        full_parameters["omega_range_average"] = sys.argv[2]
        param_lines = create_XDS_INP(full_parameters, sys.argv[1])
        open("XDS.INP", 'w').writelines(param_lines)
        print help()

Old way of processing Eiger data with XDS i.e. using H5ToXds

Since the release of NEGGIA, a plugin for XDS that parallelizes the reading of images from HDF5 data, conversion to H5ToXds is not required anymore. The sections below are thus largely obsolete.

Dectris provides a library H5ToXds (Linux only!) which is needed by XDS. That program converts (as the name indicates) the HDF5 files to CBF files; however, it does not write the geometry and other information into the CBF header (therefore, generate_XDS.INP does not work with these files). As an alternative, one could use GlobalPhasing's hdf2mini-cbf program (needs autoPROC license) or, from http://www.mrc-lmb.cam.ac.uk/harry/imosflm/ver721/downloads, the eiger2cbf-osx or eiger2cbf-linux program written by T. Nakane. These programs do write a useful CBF header.

For faster processing (Linux only; script needs to be adapted for OSX), the shell script below should be copied to /usr/local/bin/H5ToXds and made executable (chmod a+rx /usr/local/bin/H5ToXds*). The binary H5ToXds then should be named e.g. /usr/local/bin/H5ToXds.bin - note the .bin filename extension! The script also uses RAM to speed up processing; it uses it for fast storage of the temporary CBF file that H5ToXds/eiger2cbf/hdf2mini-cbf writes, and that each parallel thread ("processor") of XDS reads. The amount of additional RAM this requires is modest (about (number of pixels)*(number of threads) bytes).

Benchmark using H5ToXds

The numbers below refer to the H5ToXds binary as used in the script below.

The timing, using the XDS (BUILT=20151231), is on the first run

INIT:  elapsed wall-clock time       12.0 sec
COLSPOT: elapsed wall-clock time       44.9 sec
INTEGRATE: total elapsed wall-clock time       65.1 sec
CORRECT: elapsed wall-clock time        2.9 sec
Total elapsed wall-clock time for XDS      133.6 sec

When I repeat this, I get

Total elapsed wall-clock time for XDS      128.3 sec

Repeat once again:

Total elapsed wall-clock time for XDS      129.3 sec

So a bit of cache-warming helps, but not much. This machine has 64GB RAM. From the output of "top", the highest memory usage occurs during INTEGRATE, when each of the mintegrate_par processes consumes up to 7.4% of the memory. In other words, in this way less than 20GB of total memory are used. "top" shows a CPU consumption around (on average) 4 times 650%.

The number of JOBs and PROCESSORs could be optimized. I tried 6 JOBs and get

Total elapsed wall-clock time for XDS      120.1 sec

so there's still some room for improvement.

With program versions as of 2016-03-10, eiger2cbf-linux is practically as fast as the H5ToXds binary; hdf2mini-cbf is somewhat slower.

When unpacking the .h5 files to .cbf files and processing those, I get on the same machine and with same processing parameters:

Total elapsed wall-clock time for XDS       96.3 sec

which indicates a 24% overhead due to the HDF5-to-CBF conversion. However, one has to add to this the time for the HDF5-to-CBF conversion, which is (with 18 parallel H5ToXds jobs each converting 50 frames) 34.2 sec, so overall the "on-the-fly" route using the script below is faster than the "pre-conversion" route, at least on this machine.

A script for faster XDS processing of Eiger data

# Kay Diederichs 10/2015
# 3/2016 adapt for eiger2cbf-linux and hdf2min-cbf
# for the latter see https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ccp4bb;58a4ee1.1603 and
# https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ccp4bb;a048b4e8.1603 
# Idea: put temporary files into fast local directory, instead of NFS
# Installation: Rename Dectris' H5ToXds to H5ToXds.bin
#               This script should be called H5ToXds and reside in $PATH 
#               Modify this script according to which binary you use - see comments below.
# Recommendation:
# - for the fast local directory one should use a RAMdisk (one GB size at most)
# - /dev/shm seems to be set up for that purpose on most distributions
# choose between H5ToXds.bin,  eiger2cbf and hdf2mini-cbf; un/comment accordingly 
/usr/local/bin/H5ToXds.bin $1 $2 "$tempfile" || rm "$tempfile"
#/usr/local/bin/eiger2cbf-linux $1 $2 "$tempfile" >& /dev/null  || rm "$tempfile"
#/usr/local/bin/hdf2mini-cbf $1 $2 "$tempfile"  || rm "$tempfile"
ln -sf "$tempfile" $3 2>/dev/null

See also