cctbx Multi-platform Build Results

Build tag: 2004_02_17_1720

Binary distributions

irix65_mipspro7312_py222 test.log build.log

irix65_mipspro741_py222 test.log build.log

mac_os_10_2_py23 selfx test.log build.log [Mac OS X notes]

mac_os_10_3 selfx test.log build.log [Mac OS X notes]

redhat73_py222 test.log build.log

redhat80 selfx test.log build.log

redhat80_py23 selfx test.log build.log

redhat90 selfx test.log build.log

redhat90_py23 selfx test.log build.log

tru64_py222 test.log build.log

tru64_py23 test.log build.log

win2000_py222 test.log build.log

win2000_py23 test.log build.log

win_xp_py222 exe test.log build.log (also known to work under Windows 2000)

win_xp_py233 exe test.log build.log (also known to work under Windows 2000)

[Other platforms]

On platforms with a choice of Python 2.2.2 or Python 2.3 use the Python 2.3 bundle if you have no preference. For Windows get the bundle that matches your existing Python installation. Download the desired selfx or exe self-extracting binary distribution to a new, empty directory. Avoid directories with spaces in the pathname. In particular, under Windows do not install under C:\Program Files.

Under Unix install a selfx distribution using the perl command, for example:

perl cctbx_redhat80.selfx
Under Windows exe distributions are installed simply by running them (the matching Python version must be installed already).

The installer must be run on the platform where it is used. The installed package may be used over the network, but only if the pathnames to the installation directory are identical on the server and the client.

[Installation overview]

Self-extracting cctbx and Python 2.3 sources for Unix

cctbx_python_23_bundle.selfx

Download the file and run the following command in a new, empty directory:
perl cctbx_python_23_bundle.selfx
This installs Python 2.3 and all cctbx modules from scratch.

Alternative for manual installation: cctbx_python_23_bundle.tar.gz [Installation instructions]

Self-extracting cctbx and Python 2.2.2 sources for Unix

cctbx_python_222_bundle.selfx

Download the file and run the following command in a new, empty directory:
perl cctbx_python_222_bundle.selfx
This installs Python 2.2.2 and all cctbx modules from scratch.

Alternative for manual installation: cctbx_python_222_bundle.tar.gz [Installation instructions]

Self-extracting cctbx sources for Unix

cctbx_bundle.selfx

Download the file and run the following command in a new, empty directory:
perl cctbx_bundle.selfx
This installs all cctbx modules from scratch. Python 2.2.1 or higher must be pre-installed on the target machine. Known to work under Redhat 8.0 with the Python version that ships with the operating system (/usr/bin/python).

Alternative for manual installation: cctbx_bundle.tar.gz [Installation instructions]

changes.txt

cctbx.xray:
  - new xray.structure.asu_mappings() method
  - sampled_model_density, fast_gradients:
      - speed improvements:
          - more sophisticated determination of sampling box
          - loop over points in sampling box optimized by pre-computing
            some intermediates
          - manually inlined code
      - distinction between wing_cutoff and rho_cutoff
          - rho_cutoff is determined as the average Gaussian
            Fourier transform at zero * wing_cutoff
            and the same rho_cutoff is used for all scatterers
      - default wing_cutoff=1.e-3
      - distinction of u_base and u_extra:
          - u_extra determined as u_base - u_radius_min
      - sampling of negative density is now supported

cctbx.crystal:
  - find_best_cell: faster algorithm for monoclinic space groups
  - new crystal.direct_space_asu (floating-point parameterization)
  - new crystal.direct_space_asu.asu_mappings
  - new crystal.neighbors_simple_pair_generator
  - new crystal.neighbors_fast_pair_generator

cctbx.miller:
  - binner.bin_legend(): support formatting of binned data
  - class binned_data, grouping the binner and binned data
  - set.completeness(use_binning=True)
  - set.use_binning_of(): same binning for different array of miller indices

cctbx.euclidean_model_matching:
  - new model_matches.transform_model()

cctbx.uctbx:
  - new unit_cell::shortest_vector_sq()

scitbx.math:
  - new scitbx.math.minimum_covering_sphere
    (Python N-dimensional, C++ 3-dimensional)
  - new scitbx::math::bessel::i1_over_i0()

Moved from cctbx to scitbx:
  - array_family.flex.vec3_double
  - cctbx.matrix -> scitbx.matrix

libtbx:
  - warn if scons is in source directory but packages required
    for build are missing
  - libtbx.itertbx: wrapper/emulator for Python 2.3 itertools
  - use -ffast-math (gcc) and -fast (Tru64 cxx)

Latest Boost CVS (2004_02_17_1555)
Latest SCons CVS (2004_02_17_1546)