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Authors:
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    Ion Vasilief</description>
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dimensions, the results are rather spotty, but you might try it
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The library implements a number of different data structures, based on
kd-trees and box-decomposition trees, and employs a couple of
different search strategies.

The library also comes with test programs for measuring the quality of
performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.

Authors:
---------
David M. Mount
Sunil Arya</description>
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algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions.

In the nearest neighbor problem a set of data points in d-dimensional
space is given. These points are preprocessed into a data structure,
so that given any query point q, the nearest or generally k nearest
points of P to q can be reported efficiently. The distance between two
points can be defined in many ways. ANN assumes that distances are
measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance.

Based on our own experience, ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20. (For applications in significantly higher
dimensions, the results are rather spotty, but you might try it
anyway.)

The library implements a number of different data structures, based on
kd-trees and box-decomposition trees, and employs a couple of
different search strategies.

The library also comes with test programs for measuring the quality of
performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.

Authors:
---------
David M. Mount
Sunil Arya</description>
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algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions.

In the nearest neighbor problem a set of data points in d-dimensional
space is given. These points are preprocessed into a data structure,
so that given any query point q, the nearest or generally k nearest
points of P to q can be reported efficiently. The distance between two
points can be defined in many ways. ANN assumes that distances are
measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance.

Based on our own experience, ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20. (For applications in significantly higher
dimensions, the results are rather spotty, but you might try it
anyway.)

The library implements a number of different data structures, based on
kd-trees and box-decomposition trees, and employs a couple of
different search strategies.

The library also comes with test programs for measuring the quality of
performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.

Authors:
---------
David M. Mount
Sunil Arya</description>
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in arbitrarily high dimensions.

In the nearest neighbor problem a set of data points in d-dimensional
space is given. These points are preprocessed into a data structure,
so that given any query point q, the nearest or generally k nearest
points of P to q can be reported efficiently. The distance between two
points can be defined in many ways. ANN assumes that distances are
measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance.

Based on our own experience, ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20. (For applications in significantly higher
dimensions, the results are rather spotty, but you might try it
anyway.)

The library implements a number of different data structures, based on
kd-trees and box-decomposition trees, and employs a couple of
different search strategies.

The library also comes with test programs for measuring the quality of
performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.

This package contains header files and the static library.

Authors:
---------
David M. Mount
Sunil Arya</description>
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algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions.

In the nearest neighbor problem a set of data points in d-dimensional
space is given. These points are preprocessed into a data structure,
so that given any query point q, the nearest or generally k nearest
points of P to q can be reported efficiently. The distance between two
points can be defined in many ways. ANN assumes that distances are
measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance.

Based on our own experience, ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20. (For applications in significantly higher
dimensions, the results are rather spotty, but you might try it
anyway.)

The library implements a number of different data structures, based on
kd-trees and box-decomposition trees, and employs a couple of
different search strategies.

The library also comes with test programs for measuring the quality of
performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.

This package contains header files and the static library.

Authors:
---------
David M. Mount
Sunil Arya</description>
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  <summary>Approximate nearest neighbour search</summary>
  <description>ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions.

In the nearest neighbor problem a set of data points in d-dimensional
space is given. These points are preprocessed into a data structure,
so that given any query point q, the nearest or generally k nearest
points of P to q can be reported efficiently. The distance between two
points can be defined in many ways. ANN assumes that distances are
measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance.

Based on our own experience, ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20. (For applications in significantly higher
dimensions, the results are rather spotty, but you might try it
anyway.)

The library implements a number of different data structures, based on
kd-trees and box-decomposition trees, and employs a couple of
different search strategies.

The library also comes with test programs for measuring the quality of
performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.

This package contains the documentation.

Authors:
---------
David M. Mount
Sunil Arya</description>
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    <rpm:buildhost>build03</rpm:buildhost>
    <rpm:sourcerpm>ann-1.1.2-4.1.src.rpm</rpm:sourcerpm>
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  <summary>Approximate nearest neighbour search</summary>
  <description>ANN is a library written in C++, which supports data structures and
algorithms for both exact and approximate nearest neighbor searching
in arbitrarily high dimensions.

In the nearest neighbor problem a set of data points in d-dimensional
space is given. These points are preprocessed into a data structure,
so that given any query point q, the nearest or generally k nearest
points of P to q can be reported efficiently. The distance between two
points can be defined in many ways. ANN assumes that distances are
measured using any class of distance functions called Minkowski
metrics. These include the well known Euclidean distance, Manhattan
distance, and max distance.

Based on our own experience, ANN performs quite efficiently for point
sets ranging in size from thousands to hundreds of thousands, and in
dimensions as high as 20. (For applications in significantly higher
dimensions, the results are rather spotty, but you might try it
anyway.)

The library implements a number of different data structures, based on
kd-trees and box-decomposition trees, and employs a couple of
different search strategies.

The library also comes with test programs for measuring the quality of
performance of ANN on any particular data sets, as well as programs
for visualizing the structure of the geometric data structures.

This package contains the documentation.

Authors:
---------
David M. Mount
Sunil Arya</description>
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  <url>http://www.cs.umd.edu/~mount/ANN</url>
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of programs that allows researchers to determine macromolecular
structures by X-ray crystallography, and other biophysical techniques.
CCP4 aims to develop and support the development of cutting edge
approaches to experimental determination and analysis of protein
structure, and integrate these approaches into the suite. CCP4 is a
community based resource that supports the widest possible researcher
community, embracing academic, not for profit, and for profit
research. CCP4 aims to play a key role in the education and training
of scientists in experimental structural biology. It encourages the
wide dissemination of new ideas, techniques and practice.

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CCP4 aims to develop and support the development of cutting edge
approaches to experimental determination and analysis of protein
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community based resource that supports the widest possible researcher
community, embracing academic, not for profit, and for profit
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wide dissemination of new ideas, techniques and practice.

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CCP4 aims to develop and support the development of cutting edge
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community based resource that supports the widest possible researcher
community, embracing academic, not for profit, and for profit
research. CCP4 aims to play a key role in the education and training
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wide dissemination of new ideas, techniques and practice.

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CCP4 aims to develop and support the development of cutting edge
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community, embracing academic, not for profit, and for profit
research. CCP4 aims to play a key role in the education and training
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  <summary>Cross-crystal Likelihood Phase Probability Estimation and Refinement</summary>
  <description>There are currently two major pressures on crystallographic computing:

    * Increased automation to increase throughput in line with
      genomics applications.

    * Increased data complexity, as data from more sources is
      combined and carried through the whole calculation to solve
      more difficult problems.

The Clipper project is an initiative to address these pressures.

The aim of the project is to produce a set of object-oriented
libraries for the organisation of crystallographic data and
the performance of crystallographic computation. The libraries
are designed as a framework for new crystallographic software,
which will allow the full power of modern programming
techniques to be exploited by the developer. This will lead to
greater functionality from simpler code which will be easier
to develop and debug.

Authors
--------
    Kevin Cowtan</description>
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    * Increased automation to increase throughput in line with
      genomics applications.

    * Increased data complexity, as data from more sources is
      combined and carried through the whole calculation to solve
      more difficult problems.

The Clipper project is an initiative to address these pressures.

The aim of the project is to produce a set of object-oriented
libraries for the organisation of crystallographic data and
the performance of crystallographic computation. The libraries
are designed as a framework for new crystallographic software,
which will allow the full power of modern programming
techniques to be exploited by the developer. This will lead to
greater functionality from simpler code which will be easier
to develop and debug.

Authors
--------
    Kevin Cowtan</description>
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    * Increased automation to increase throughput in line with
      genomics applications.

    * Increased data complexity, as data from more sources is
      combined and carried through the whole calculation to solve
      more difficult problems.

The Clipper project is an initiative to address these pressures.

The aim of the project is to produce a set of object-oriented
libraries for the organisation of crystallographic data and
the performance of crystallographic computation. The libraries
are designed as a framework for new crystallographic software,
which will allow the full power of modern programming
techniques to be exploited by the developer. This will lead to
greater functionality from simpler code which will be easier
to develop and debug.

Authors
--------
    Kevin Cowtan</description>
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  <description>There are currently two major pressures on crystallographic computing:

    * Increased automation to increase throughput in line with
      genomics applications.

    * Increased data complexity, as data from more sources is
      combined and carried through the whole calculation to solve
      more difficult problems.

The Clipper project is an initiative to address these pressures.

The aim of the project is to produce a set of object-oriented
libraries for the organisation of crystallographic data and
the performance of crystallographic computation. The libraries
are designed as a framework for new crystallographic software,
which will allow the full power of modern programming
techniques to be exploited by the developer. This will lead to
greater functionality from simpler code which will be easier
to develop and debug.

This package contains headers for development.

Authors
--------
    Kevin Cowtan</description>
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    * Increased automation to increase throughput in line with
      genomics applications.

    * Increased data complexity, as data from more sources is
      combined and carried through the whole calculation to solve
      more difficult problems.

The Clipper project is an initiative to address these pressures.

The aim of the project is to produce a set of object-oriented
libraries for the organisation of crystallographic data and
the performance of crystallographic computation. The libraries
are designed as a framework for new crystallographic software,
which will allow the full power of modern programming
techniques to be exploited by the developer. This will lead to
greater functionality from simpler code which will be easier
to develop and debug.

This package contains headers for development.

Authors
--------
    Kevin Cowtan</description>
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    * Increased automation to increase throughput in line with
      genomics applications.

    * Increased data complexity, as data from more sources is
      combined and carried through the whole calculation to solve
      more difficult problems.

The Clipper project is an initiative to address these pressures.

The aim of the project is to produce a set of object-oriented
libraries for the organisation of crystallographic data and
the performance of crystallographic computation. The libraries
are designed as a framework for new crystallographic software,
which will allow the full power of modern programming
techniques to be exploited by the developer. This will lead to
greater functionality from simpler code which will be easier
to develop and debug.

This package contains example programs.

Authors
--------
    Kevin Cowtan</description>
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    * Increased data complexity, as data from more sources is
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      more difficult problems.

The Clipper project is an initiative to address these pressures.

The aim of the project is to produce a set of object-oriented
libraries for the organisation of crystallographic data and
the performance of crystallographic computation. The libraries
are designed as a framework for new crystallographic software,
which will allow the full power of modern programming
techniques to be exploited by the developer. This will lead to
greater functionality from simpler code which will be easier
to develop and debug.

This package contains example programs.

Authors
--------
    Kevin Cowtan</description>
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Authors
--------
    Paul Emsley</description>
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Coot displays maps and models and allows model manipulations such as
idealization, real space refinement, manual rotation/translation,
rigid-body fitting, ligand search, solvation, mutations, rotamers,
Ramachandran plots, skeletonization...

Authors
--------
    Paul Emsley</description>
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Coot displays maps and models and allows model manipulations such as
idealization, real space refinement, manual rotation/translation,
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Authors
--------
    Paul Emsley</description>
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      <rpm:entry name="libcoot-coord-extras.so.0()(64bit)"/>
      <rpm:entry name="libatk-1.0.so.0()(64bit)"/>
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      <rpm:entry name="libcairo.so.2()(64bit)"/>
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      <rpm:entry name="libpangoft2-1.0.so.0()(64bit)"/>
      <rpm:entry name="libcoot-coord-utils.so.0()(64bit)"/>
      <rpm:entry name="libgailutil.so.18()(64bit)"/>
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</package>
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  <description>This package provides debug information for package coot.
Debug information is useful when developing applications that use this
package or when debugging this package.</description>
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  <url>http://biop.ox.ac.uk/coot/</url>
  <time file="1330418991" build="1330418871"/>
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<location href="i586/coot-debuginfo-0.6.2-4.2.i586.rpm"/>
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  <description>This package provides debug information for package coot.
Debug information is useful when developing applications that use this
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  <url>http://biop.ox.ac.uk/coot/</url>
  <time file="1330419673" build="1330419573"/>
  <size package="20837900" installed="87930480" archive="87965120"/>
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    <file>/usr/lib/debug/usr/bin/findligand-real.debug</file>
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  <description>This package provides debug sources for package coot.
Debug sources are useful when developing applications that use this
package or when debugging this package.</description>
  <packager/>
  <url>http://biop.ox.ac.uk/coot/</url>
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  <description>This package provides debug sources for package coot.
Debug sources are useful when developing applications that use this
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  <url>http://biop.ox.ac.uk/coot/</url>
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Coot displays maps and models and allows model manipulations such as
idealization, real space refinement, manual rotation/translation,
rigid-body fitting, ligand search, solvation, mutations, rotamers,
Ramachandran plots, skeletonization...

This package contains headers and libraries needed for development.

Authors
--------
    Paul Emsley</description>
  <packager/>
  <url>http://biop.ox.ac.uk/coot/</url>
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  <summary>Crystallographic Object-Oriented Toolkit</summary>
  <description>Coot is for macromolecular model building, model completion and
validation, particularly suitable for protein modelling using X-ray
data.

Coot displays maps and models and allows model manipulations such as
idealization, real space refinement, manual rotation/translation,
rigid-body fitting, ligand search, solvation, mutations, rotamers,
Ramachandran plots, skeletonization...

This package contains headers and libraries needed for development.

Authors
--------
    Paul Emsley</description>
  <packager/>
  <url>http://biop.ox.ac.uk/coot/</url>
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<package type="rpm">
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validation, particularly suitable for protein modelling using X-ray
data.

Coot displays maps and models and allows model manipulations such as
idealization, real space refinement, manual rotation/translation,
rigid-body fitting, ligand search, solvation, mutations, rotamers,
Ramachandran plots, skeletonization...

This package contains shared libraries.

Authors
--------
    Paul Emsley</description>
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Coot displays maps and models and allows model manipulations such as
idealization, real space refinement, manual rotation/translation,
rigid-body fitting, ligand search, solvation, mutations, rotamers,
Ramachandran plots, skeletonization...

This package contains shared libraries.

Authors
--------
    Paul Emsley</description>
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Authors:
--------
    Benoît Guillon &lt;marsgui@users.sourceforge.net&gt;
    Andreas Hoenen &lt;andreas.hoenen@arcor.de&gt;</description>
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Authors:
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    Benoît Guillon &lt;marsgui@users.sourceforge.net&gt;
    Andreas Hoenen &lt;andreas.hoenen@arcor.de&gt;</description>
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standardize secondary structure assignment. DSSP is a database of
secondary structure assignments (and much more) for all protein
entries in the Protein Data Bank (PDB). DSSP is also the program that
calculates DSSP entries from PDB entries. DSSP does not predict
secondary structure.

Author:
Maarten Hekkelman</description>
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calculates DSSP entries from PDB entries. DSSP does not predict
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Author:
Maarten Hekkelman</description>
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  <summary>A molecular dynamics package, modified</summary>
  <description>GROMACS is a versatile and extremely well optimized package to perform
molecular dynamics computer simulations and subsequent trajectory
analysis. It is developed for biomolecules like proteins, but the
extremely high performance means it is used also in several other
field like polymer chemistry and solid state physics. This version has
the dynamic libs and executables; to hack new utility programs you
also need the headers and static libs in gromacs-dev.

This is a modified version for use with pymacs.

Authors:
--------
    David van der Spoel &lt;spoel@gromacs.org&gt;
    Berk Hess &lt;hess@gromacs.org&gt;
    Erik Lindahl &lt;lindahl@gromacs.org&gt;</description>
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  <url/>
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    <file>/usr/lib/gromacs-pymacs/bin/anadock</file>
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    <file>/usr/lib/gromacs-pymacs/bin/g_velacc</file>
    <file>/usr/lib/gromacs-pymacs/bin/g_wham</file>
    <file>/usr/lib/gromacs-pymacs/bin/genbox</file>
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  <summary>A molecular dynamics package, modified</summary>
  <description>GROMACS is a versatile and extremely well optimized package to perform
molecular dynamics computer simulations and subsequent trajectory
analysis. It is developed for biomolecules like proteins, but the
extremely high performance means it is used also in several other
field like polymer chemistry and solid state physics. This version has
the dynamic libs and executables; to hack new utility programs you
also need the headers and static libs in gromacs-dev.

This is a modified version for use with pymacs.

Authors:
--------
    David van der Spoel &lt;spoel@gromacs.org&gt;
    Berk Hess &lt;hess@gromacs.org&gt;
    Erik Lindahl &lt;lindahl@gromacs.org&gt;</description>
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  <description>GROMACS is a versatile and extremely well optimized package to perform
molecular dynamics computer simulations and subsequent trajectory
analysis. It is developed for biomolecules like proteins, but the
extremely high performance means it is used also in several other
field like polymer chemistry and solid state physics. This version has
the dynamic libs and executables; to hack new utility programs you
also need the headers and static libs in gromacs-dev.

This is a modified version for use with pymacs.

Authors:
--------
    David van der Spoel &lt;spoel@gromacs.org&gt;
    Berk Hess &lt;hess@gromacs.org&gt;
    Erik Lindahl &lt;lindahl@gromacs.org&gt;</description>
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Authors:
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Authors
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    Kevin Cowtan</description>
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The Clipper project is an initiative to address these pressures.

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which will allow the full power of modern programming
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Authors
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    Kevin Cowtan</description>
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Authors
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    Summary
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    MEAD is written in C++, which is a significant departure from most
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    a software system that other people could borrow pieces from and
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    MEAD is free software.  You can redistribute it and/or modify it
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Authors:
--------
        Donald Bashford
        don.bashford@stjude.org

        Hartwell Center
        Saint Jude Children's Research Hospital
        Memphis, TN</description>
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    Summary
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Authors:
--------
        Donald Bashford
        don.bashford@stjude.org

        Hartwell Center
        Saint Jude Children's Research Hospital
        Memphis, TN</description>
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    Summary
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    which the solvent and the molecular interior have different
    dielectric constants, the boundary between the different
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Authors:
--------
        Donald Bashford
        don.bashford@stjude.org

        Hartwell Center
        Saint Jude Children's Research Hospital
        Memphis, TN</description>
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    Summary
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    a software system that other people could borrow pieces from and
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    MEAD is free software.  You can redistribute it and/or modify it
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    the Free Software Foundation; either version 1, or (at your
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    use MEAD in a scientific publication, please cite the papers,
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    This package contains header files and a library.

Authors:
--------
        Donald Bashford
        don.bashford@stjude.org

        Hartwell Center
        Saint Jude Children's Research Hospital
        Memphis, TN</description>
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    MEAD is free software.  You can redistribute it and/or modify it
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    This package contains header files and a library.

Authors:
--------
        Donald Bashford
        don.bashford@stjude.org

        Hartwell Center
        Saint Jude Children's Research Hospital
        Memphis, TN</description>
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metadynamics, umbrella sampling and Jarzynski-equation based steered
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It consists of a set of base classes and a library of flexible and
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Authors:
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It consists of a set of base classes and a library of flexible and
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Authors:
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Authors:
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Authors:
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Authors:
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    <rpm:license>CNRI Python License</rpm:license>
    <rpm:vendor>openSUSE Build Service</rpm:vendor>
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    <rpm:buildhost>build24</rpm:buildhost>
    <rpm:sourcerpm>pymacs-0.4-6.3.src.rpm</rpm:sourcerpm>
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      <rpm:entry name="debuginfo(build-id)" flags="EQ" epoch="0" ver="ceba941bbf5a6617aa464fc2eb5514a9d2aea011"/>
      <rpm:entry name="pymacs-debuginfo" flags="EQ" epoch="0" ver="0.4" rel="6.3"/>
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</package>
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  <checksum type="sha" pkgid="YES">f37ec134a2d3c552796da52eba3a819a02f0bb0b</checksum>
  <summary>Debug sources for package pymacs</summary>
  <description>This package provides debug sources for package pymacs.
Debug sources are useful when developing applications that use this
package or when debugging this package.</description>
  <packager/>
  <url/>
  <time file="1331297424" build="1331297391"/>
  <size package="13870" installed="49034" archive="50332"/>
<location href="i586/pymacs-debugsource-0.4-6.3.i586.rpm"/>
  <format>
    <rpm:license>CNRI Python License</rpm:license>
    <rpm:vendor>openSUSE Build Service</rpm:vendor>
    <rpm:group>Development/Debug</rpm:group>
    <rpm:buildhost>build17</rpm:buildhost>
    <rpm:sourcerpm>pymacs-0.4-6.3.src.rpm</rpm:sourcerpm>
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</package>
<package type="rpm">
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  <checksum type="sha" pkgid="YES">fc118d20d4784973966f64819014d84a929b7f35</checksum>
  <summary>Debug sources for package pymacs</summary>
  <description>This package provides debug sources for package pymacs.
Debug sources are useful when developing applications that use this
package or when debugging this package.</description>
  <packager/>
  <url/>
  <time file="1331295962" build="1331295890"/>
  <size package="13851" installed="49034" archive="50332"/>
<location href="x86_64/pymacs-debugsource-0.4-6.3.x86_64.rpm"/>
  <format>
    <rpm:license>CNRI Python License</rpm:license>
    <rpm:vendor>openSUSE Build Service</rpm:vendor>
    <rpm:group>Development/Debug</rpm:group>
    <rpm:buildhost>build24</rpm:buildhost>
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</package>
<package type="rpm">
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  <arch>i586</arch>
  <version epoch="0" ver="1.2.0" rel="2.1"/>
  <checksum type="sha" pkgid="YES">9339c5df39e939101d29e80382c914613065aebe</checksum>
  <summary>A Molecular Viewer</summary>
  <description>PyMOL is a molecular graphics system targetted at medium to large
biomolecules like proteins. It can generate high-quality
publication-ready molecular graphics images and animations.

Features include:

* Visualization of molecules, molecular trajectories and surfaces
  of crystallography data or orbitals
* Molecular builder and sculptor
* Internal raytracer and movie generator
* Fully extensible and scriptable via a python interface

File formats PyMOL can read include PDB, XYZ, CIF, MDL Molfile,
ChemDraw, CCP4 maps, XPLOR maps and Gaussian cube maps.


Authors:
--------
    Warren L. DeLano &lt;warren@delanoscientific.com&gt;</description>
  <packager/>
  <url>http://www.pymol.org/</url>
  <time file="1285862811" build="1285862778"/>
  <size package="5590090" installed="19228337" archive="19373268"/>
<location href="i586/pymol-1.2.0-2.1.i586.rpm"/>
  <format>
    <rpm:license>CNRI Python License</rpm:license>
    <rpm:vendor>openSUSE Build Service</rpm:vendor>
    <rpm:group>Productivity/Scientific/Chemistry</rpm:group>
    <rpm:buildhost>build34</rpm:buildhost>
    <rpm:sourcerpm>pymol-1.2.0-2.1.src.rpm</rpm:sourcerpm>
    <rpm:header-range start="440" end="76474"/>
    <rpm:provides>
      <rpm:entry name="PyMOL"/>
      <rpm:entry name="_champ.so"/>
      <rpm:entry name="_cmd.so"/>
      <rpm:entry name="_glu.so"/>
      <rpm:entry name="_glu_num.so"/>
      <rpm:entry name="_glut.so"/>
      <rpm:entry name="_opengl.so"/>
      <rpm:entry name="_opengl_num.so"/>
      <rpm:entry name="openglutil.so"/>
      <rpm:entry name="openglutil_num.so"/>
      <rpm:entry name="pymol" flags="EQ" epoch="0" ver="1.2.0" rel="2.1"/>
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    <rpm:requires>
      <rpm:entry name="libc.so.6(GLIBC_2.3)"/>
      <rpm:entry name="python" flags="GE" epoch="0" ver="2.6"/>
      <rpm:entry name="libfreetype.so.6"/>
      <rpm:entry name="libpthread.so.0(GLIBC_2.0)"/>
      <rpm:entry name="libpng12.so.0"/>
      <rpm:entry name="libc.so.6"/>
      <rpm:entry name="libz.so.1"/>
      <rpm:entry name="tk"/>
      <rpm:entry name="libpthread.so.0(GLIBC_2.2)"/>
      <rpm:entry name="libc.so.6(GLIBC_2.2)"/>
      <rpm:entry name="libm.so.6"/>
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      <rpm:entry name="libGLU.so.1"/>
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      <rpm:entry name="libc.so.6(GLIBC_2.1.3)"/>
      <rpm:entry name="python-numpy"/>
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      <rpm:entry name="libm.so.6(GLIBC_2.0)"/>
      <rpm:entry name="libpython2.6.so.1.0"/>
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      <rpm:entry name="libgcc_s.so.1(GCC_3.0)"/>
      <rpm:entry name="libstdc++.so.6"/>
      <rpm:entry name="Pmw"/>
      <rpm:entry name="/bin/csh"/>
      <rpm:entry name="libgcc_s.so.1"/>
      <rpm:entry name="libglut.so.3"/>
      <rpm:entry name="/bin/sh"/>
      <rpm:entry name="/usr/bin/python"/>
      <rpm:entry name="libc.so.6(GLIBC_2.0)"/>
      <rpm:entry name="libgcc_s.so.1(GLIBC_2.0)"/>
      <rpm:entry name="libpng12.so.0(PNG12_0)"/>
      <rpm:entry name="libstdc++.so.6(CXXABI_1.3)"/>
      <rpm:entry name="libc.so.6(GLIBC_2.1)"/>
      <rpm:entry name="libc.so.6(GLIBC_2.3.4)"/>
    </rpm:requires>
    <file>/usr/bin/pymol</file>
  </format>
</package>
<package type="rpm">
  <name>pymol</name>
  <arch>src</arch>
  <version epoch="0" ver="1.2.0" rel="2.1"/>
  <checksum type="sha" pkgid="YES">30654600dcf8c2e2765ffe3806ea89b14f0f254a</checksum>
  <summary>A Molecular Viewer</summary>
  <description>PyMOL is a molecular graphics system targetted at medium to large
biomolecules like proteins. It can generate high-quality
publication-ready molecular graphics images and animations.

Features include:

* Visualization of molecules, molecular trajectories and surfaces
  of crystallography data or orbitals
* Molecular builder and sculptor
* Internal raytracer and movie generator
* Fully extensible and scriptable via a python interface

File formats PyMOL can read include PDB, XYZ, CIF, MDL Molfile,
ChemDraw, CCP4 maps, XPLOR maps and Gaussian cube maps.


Authors:
--------
    Warren L. DeLano &lt;warren@delanoscientific.com&gt;</description>
  <packager/>
  <url>http://www.pymol.org/</url>
  <time file="1285862860" build="1285862833"/>
  <size package="6898652" installed="6931244" archive="6931896"/>
<location href="src/pymol-1.2.0-2.1.src.rpm"/>
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    <rpm:vendor>openSUSE Build Service</rpm:vendor>
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    <rpm:requires>
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      <rpm:entry name="freetype2-devel"/>
      <rpm:entry name="fdupes"/>
      <rpm:entry name="libpng-devel"/>
      <rpm:entry name="tcl-devel"/>
      <rpm:entry name="python-devel" flags="GE" epoch="0" ver="2.1"/>
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</package>
<package type="rpm">
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  <version epoch="0" ver="1.2.0" rel="2.1"/>
  <checksum type="sha" pkgid="YES">406df12dfab750312ff8f63eb7e4cb1ec4d86c30</checksum>
  <summary>A Molecular Viewer</summary>
  <description>PyMOL is a molecular graphics system targetted at medium to large
biomolecules like proteins. It can generate high-quality
publication-ready molecular graphics images and animations.

Features include:

* Visualization of molecules, molecular trajectories and surfaces
  of crystallography data or orbitals
* Molecular builder and sculptor
* Internal raytracer and movie generator
* Fully extensible and scriptable via a python interface

File formats PyMOL can read include PDB, XYZ, CIF, MDL Molfile,
ChemDraw, CCP4 maps, XPLOR maps and Gaussian cube maps.


Authors:
--------
    Warren L. DeLano &lt;warren@delanoscientific.com&gt;</description>
  <packager/>
  <url>http://www.pymol.org/</url>
  <time file="1285862860" build="1285862833"/>
  <size package="5724622" installed="19373435" archive="19519856"/>
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      <rpm:entry name="openglutil.so()(64bit)"/>
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      <rpm:entry name="python" flags="GE" epoch="0" ver="2.6"/>
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      <rpm:entry name="tk"/>
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      <rpm:entry name="libm.so.6(GLIBC_2.2.5)(64bit)"/>
      <rpm:entry name="libm.so.6()(64bit)"/>
      <rpm:entry name="python-numpy"/>
      <rpm:entry name="/bin/sh"/>
      <rpm:entry name="libfreetype.so.6()(64bit)"/>
      <rpm:entry name="libgcc_s.so.1(GCC_3.0)(64bit)"/>
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      <rpm:entry name="libc.so.6()(64bit)"/>
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      <rpm:entry name="libgcc_s.so.1()(64bit)"/>
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      <rpm:entry name="libpython2.6.so.1.0()(64bit)"/>
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<package type="rpm">
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  <description>This package provides debug information for package pymol.
Debug information is useful when developing applications that use this
package or when debugging this package.</description>
  <packager/>
  <url>http://www.pymol.org/</url>
  <time file="1285862812" build="1285862778"/>
  <size package="2094383" installed="6902657" archive="6912152"/>
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    <rpm:vendor>openSUSE Build Service</rpm:vendor>
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</package>
<package type="rpm">
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  <summary>Debug information for package pymol</summary>
  <description>This package provides debug information for package pymol.
Debug information is useful when developing applications that use this
package or when debugging this package.</description>
  <packager/>
  <url>http://www.pymol.org/</url>
  <time file="1285862861" build="1285862833"/>
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<location href="x86_64/pymol-debuginfo-1.2.0-2.1.x86_64.rpm"/>
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</package>
<package type="rpm">
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  <version epoch="0" ver="1.2.0" rel="2.1"/>
  <checksum type="sha" pkgid="YES">ac36a4347e40a9c71d8ed9858d0dc283b6dc4748</checksum>
  <summary>Debug sources for package pymol</summary>
  <description>This package provides debug sources for package pymol.
Debug sources are useful when developing applications that use this
package or when debugging this package.</description>
  <packager/>
  <url>http://www.pymol.org/</url>
  <time file="1285862813" build="1285862778"/>
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    <rpm:vendor>openSUSE Build Service</rpm:vendor>
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  <checksum type="sha" pkgid="YES">cee0fc7095f464515543b499072e79d997afa2f6</checksum>
  <summary>Debug sources for package pymol</summary>
  <description>This package provides debug sources for package pymol.
Debug sources are useful when developing applications that use this
package or when debugging this package.</description>
  <packager/>
  <url>http://www.pymol.org/</url>
  <time file="1285862861" build="1285862833"/>
  <size package="4541583" installed="25078439" archive="25133168"/>
<location href="x86_64/pymol-debugsource-1.2.0-2.1.x86_64.rpm"/>
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<package type="rpm">
  <name>python-scikits.ann</name>
  <arch>i586</arch>
  <version epoch="0" ver="0.2.dev_r803" rel="4.1"/>
  <checksum type="sha" pkgid="YES">fe941196ea7ecfde90da757c55d2091954afbc1e</checksum>
  <summary>Approximate Nearest Neighbor library wrapper for Numpy</summary>
  <description>python-scikits.ann is a SWIG-generated python wrapper for the Approximate Nearest
Neighbor (ANN) Library (http://www.cs.umd.edu/~mount/ANN/), developed
by David M. Mount and Sunil Arya. python-scikits.ann provides an immutable kdtree
implementation (via ANN) which can perform k-nearest neighbor and
approximate k-nearest neighbor searches.

Authors:
--------
    Barry Wark &lt;barrywark@gmail.com&gt;</description>
  <packager/>
  <url>http://scipy.org/scipy/scikits/wiki/AnnWrapper</url>
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Python bindings for the Qt toolkit, but can be used to create bindings
for any C or C++ library.

This package contains all the developer tools you need to create your
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Authors:
--------
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    M. Cecchini
    F. Rao
    G. Settanni
    A. Caflisch</description>
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easy and straightforward to add your own analysis module. Basically,
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Authors:
--------
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    M. Cecchini
    F. Rao
    G. Settanni
    A. Caflisch</description>
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Authors:
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    M. Cecchini
    F. Rao
    G. Settanni
    A. Caflisch</description>
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Authors:
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