Package: ants Version: 1.9.2+svn680.dfsg-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 38212 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.18, libstdc++6 (>= 4.4.0) Suggests: fsl, gridengine-client Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_1.9.2+svn680.dfsg-1~nd10.10+1_amd64.deb Size: 11326468 SHA256: a4806d17a2d77578a6a8357d428b492f79fe46e24a684ad18414177f9bbba264 SHA1: cbd5567bc66694cca62b6778b785612bf501d9ed MD5sum: 5cb2c5d85e93d091fe74f38b363eea10 Description: advanced normalization tools for brain and image analysis Advanced Normalization Tools (ANTS) is an ITK-based suite of normalization, segmentation and template-building tools for quantitative morphometric analysis. Many of the ANTS registration tools are diffeomorphic, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). Package: autotools-dev Version: 20100122.1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 216 Depends: neurodebian-popularity-contest Enhances: cdbs, debhelper Homepage: http://savannah.gnu.org/projects/config/ Priority: optional Section: devel Filename: pool/main/a/autotools-dev/autotools-dev_20100122.1~nd10.10+1_all.deb Size: 73000 SHA256: 2e4624e33e50c6bcdb26ab691a4cb339ee5d9efb514cb80d1ed34f66a224c1f3 SHA1: 4e4eeda943b73962c622a9d7222ec12799d9ad66 MD5sum: 933c51b7e10a71382d16980f9246f2a5 Description: Update infrastructure for config.{guess,sub} files This package installs an up-to-date version of config.guess and config.sub, used by the automake and libtool packages. It provides the canonical copy of those files for other packages as well. . It also documents in /usr/share/doc/autotools-dev/README.Debian.gz best practices and guidelines for using autoconf, automake and friends on Debian packages. This is a must-read for any developers packaging software that uses the GNU autotools, or GNU gettext. . Additionally this package provides seamless integration into Debhelper or CDBS, allowing maintainers to easily update config.{guess,sub} files in their packages. Package: biosig-tools Source: biosig4c++ Version: 0.96.3+svn2677-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libbiosig0, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_0.96.3+svn2677-1~nd10.10+1_amd64.deb Size: 13800 SHA256: a1db33df32adb8d90eb17c35201df81a6dbb1d834537b5e46b1b774a1cf64c79 SHA1: c4f84d4f66daeb64438276ccc0c22f7319f42711 MD5sum: 8afddeae8201279ba7cc3fd08dbb3b95 Description: format conversion tools for biomedical data formats Based on BioSig library, this package provides command line tools, such as . - save2gdf: converter between different file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF, BDF, CWFB. save2gdf can be also used to upload or retrieve data from a bscs server. Package: caret Version: 5.6.4~dfsg.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 19684 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgomp1 (>= 4.2.1), libminc2-1, libqt4-network (>= 4:4.5.3), libqt4-opengl (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvtk5.4, zlib1g (>= 1:1.2.3.3.dfsg) Recommends: qt-assistant-compat Suggests: caret-data (>= 5.6.2~dfsg.1~) Homepage: http://brainvis.wustl.edu/wiki/index.php/Caret:About Priority: optional Section: science Filename: pool/main/c/caret/caret_5.6.4~dfsg.1-1~nd10.10+1_amd64.deb Size: 7597248 SHA256: 716898d2999e390dea86131f6243e301d90072539bad0db9eeb45514ca2ebbc9 SHA1: 4484780bd5592ae49a1c6ca1f7ead91418eeee57 MD5sum: 3079243266f7620d8c242183a6f102b8 Description: Computerized Anatomical Reconstruction and Editing Toolkit This software allows for creating, viewing and manipulating surface reconstructions of the cerebral and cerebellar cortex, viewing volumes and for displaying experimental data on the surfaces and volumes. While Caret is primarily a GUI application with 'caret_command' there is also a versatile command line tool, that allows access to a substantial proportion of Caret's functionality. . Caret can download and use stereotaxic atlases (human, monkey, mouse and rat) from an open online database. . Some functionality of Caret is only available when additional data files, provided by the caret-data package, are available. This includes: . - Map volumes to surface via PALS atlas - Multi-resolution morphing - Projection of foci via PALS atlas - Surface-based registration - Surface flattening . Currently the caret-data package is only available from the NeuroDebian repository. Please see http://neuro.debian.net for more imformation. Package: cgroup-bin Source: libcgroup Version: 0.37.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 284 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libcgroup1 Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: admin Filename: pool/main/libc/libcgroup/cgroup-bin_0.37.1-1~nd10.10+1_amd64.deb Size: 65530 SHA256: e35db87d6fb8aa1f259d65cc08b348fc29796fc6a980f2915efc7bdccac15b82 SHA1: fb285e3b9fc81a2ae686f1414b32d5bde2ec413b MD5sum: d89d699ad1d4e3056909fc7d00638256 Description: Tools to control and monitor control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . These tools help manipulate, control, administrate and monitor control groups and the associated controllers. Package: classads Version: 1.0.10-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 88 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libclassad1 (= 1.0.10-2~nd10.10+1), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: misc Filename: pool/main/c/classads/classads_1.0.10-2~nd10.10+1_amd64.deb Size: 31164 SHA256: f3e921dae82a91b02923a80d8968e23e59e1506cfad99a3d5a8cec04ba6f110f SHA1: 1eb7eb110bcda7b6ff4ecc97c37926d80c350394 MD5sum: 3685b73b6a5402f789443d908124b33e Description: Condor's classad utilities A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides command line tools to manipulate, test and evaluate classads. Package: condor Version: 7.6.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11724 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.8), libclassad1, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2-1), libexpat1 (>= 1.95.8), libgcc1 (>= 1:4.1.1), libglobus-callout0, libglobus-common0, libglobus-ftp-client2, libglobus-ftp-control1, libglobus-gass-transfer2, libglobus-gram-client3, libglobus-gram-protocol3, libglobus-gsi-callback0, libglobus-gsi-cert-utils0, libglobus-gsi-credential1, libglobus-gsi-openssl-error0, libglobus-gsi-proxy-core0, libglobus-gsi-proxy-ssl1, libglobus-gsi-sysconfig1, libglobus-gss-assist3, libglobus-gssapi-error2, libglobus-gssapi-gsi4, libglobus-io3, libglobus-openssl-module0, libglobus-rsl2, libglobus-xio0, libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.7dfsg), libkrb5support0 (>= 1.7dfsg~beta2), libldap-2.4-2 (>= 2.4.7), libltdl7 (>= 2.2.6b), libpcre3 (>= 7.7), libssl0.9.8 (>= 0.9.8m-1), libstdc++6 (>= 4.4.0), libvirt0 (>= 0.5.0), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), adduser Priority: extra Section: science Filename: pool/main/c/condor/condor_7.6.0-1~nd10.10+1_amd64.deb Size: 4376814 SHA256: 9de49c6de51e152649a8518cc13a4a9835b7bb09211be2ee0832be736ff7a42c SHA1: 2543d03c9091bfd007e5b5bb3c7a790eb7b2a53e MD5sum: 93185c6c561f42f9bce27af12edc4a93 Description: workload management system Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor, Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing system, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . The Debian package uses Debconf to determine an appropriate initial configuration for a machine that shall join an existing Condor pool, and moreover, allows creating a "Personal" (single machine) Condor pool automatically. Package: condor-dbg Source: condor Version: 7.6.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 36208 Depends: neurodebian-popularity-contest, condor (= 7.6.0-1~nd10.10+1) Priority: extra Section: debug Filename: pool/main/c/condor/condor-dbg_7.6.0-1~nd10.10+1_amd64.deb Size: 12814932 SHA256: 2adcc505c5d5f5a440de1dcd43f0ff9b32a40081b8ebb3a25ae7d7166575954b SHA1: 4406ec1050ee48825e820b1c1393655927051634 MD5sum: 2c27a1f0bf5b3cdaf7201693a2832750 Description: debugging symbols for Condor Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor, Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing system, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides the debugging symbols for Condor. Package: condor-doc Source: condor Version: 7.6.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11748 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.6.0-1~nd10.10+1_all.deb Size: 5953010 SHA256: 45c6cdbdc88ffe31d29961df55384146444dee125bb1087f7e151b4bf9cbac10 SHA1: 668a3080a759a3114549bd7155cb9bea859793db MD5sum: e50d27265d851f4c3da3312cd984b901 Description: documentation for Condor Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor, Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing system, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides Condor's documentation in HTML and PDF format, as well as configuration and other examples. Package: connectomeviewer Version: 2.0.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1884 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2, ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.0.0-1~nd10.10+1_all.deb Size: 1354952 SHA256: c828ff775987da12a6eb24e0d4c1c4fa674233df656d15f6de0bcc5c7fd05794 SHA1: 06d045f36f46ca1886068b045192045d7a16530f MD5sum: bb474a0f037701ad779ee4b53822d1e4 Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: coop-computing-tools Source: cctools Version: 3.3.4-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3592 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfuse2 (>= 2.8.1), libglobus-common0, libglobus-gss-assist3, libglobus-gssapi-gsi4, libkrb5-3 (>= 1.6.dfsg.2), libmysqlclient16 (>= 5.1.21-1), libncurses5 (>= 5.7+20100313), libreadline6 (>= 6.0), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0) Suggests: coop-computing-tools-doc, condor, gridengine-client Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: utils Filename: pool/main/c/cctools/coop-computing-tools_3.3.4-1~nd10.10+1_amd64.deb Size: 1440760 SHA256: 1ca880423853375fcbcccff916675099bbef3093fe72fea13a4d0642ba6cfecb SHA1: a2b85fd537c62a47453d64c72c3e2dd66213036a MD5sum: a935b45e982c8c67567c76940f618517 Description: cooperative computing tools This is a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. This includes: . * Chirp: A personal filesystem and I/O protocol that allows unprivileged users to share space securely, efficiently, and conveniently. When combined with Parrot, Chirp allows users to create custom wide-area distributed filesystems. * Parrot: A transparent user-level virtual filesystem that allows any ordinary program to be attached to a remote storage device such as an FTP server or a Chirp server. * Makeflow: A workflow system for parallel and distributed computing that uses a language very similar to Make. * Work Queue: A system and API for building master-worker style programs that scale up to thousands of processors. * All Pairs: A computational abstraction for running very large Cartesian products. * Wavefront: A computational asbtraction for running very large dynamic programming problems. * The Fault Tolerant Shell: A high-level programming language that allows users to combine the ease of shell scripting, the power of distributed programming, and the precision of compiled languages. Basically, parallel programming and exception handling for scripts. Package: coop-computing-tools-dev Source: cctools Version: 3.3.4-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1176 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: libs Filename: pool/main/c/cctools/coop-computing-tools-dev_3.3.4-1~nd10.10+1_amd64.deb Size: 247228 SHA256: a4339e84317bae72f098ae328192f5909ec6b136785ff6cd8077133fe5caf43f SHA1: 3444d215fd240ee35506b83bb03bc9f7a101fdeb MD5sum: 65c0ff67721339f92b0015e40364e5c5 Description: libraries and header files for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides static libraries and header files for development. Package: coop-computing-tools-doc Source: cctools Version: 3.3.4-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2512 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.3.4-1~nd10.10+1_all.deb Size: 280228 SHA256: 0d38d25c3707d673c3bab7182fcfa1a9ad37819f27a06a3daaceb330f2d4dbfb SHA1: d6ac466fb3b4a696c3deec068e8471ae99f94e16 MD5sum: fa31699df2065080728fd9bb3b4086a3 Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: cython Version: 0.13-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3680 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd10.10+1_amd64.deb Size: 865752 SHA256: 2dcdbac1a90314879ef382ff5fa4ccf87630d47af9a78a4d2d2c255df192d884 SHA1: 0e747b0c464824debec67f456d27395da1616fea MD5sum: e033c32d40dd528dc4cfe07e08efa7f0 Description: C-Extensions for Python Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations. . The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code. . This makes Cython the ideal language for wrapping for external C libraries, and for fast C modules that speed up the execution of Python code. Python-Version: 2.6 Package: cython-dbg Source: cython Version: 0.13-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5324 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.3), cython (= 0.13-1~nd10.10+1) Suggests: gcc Homepage: http://cython.org/ Priority: extra Section: debug Filename: pool/main/c/cython/cython-dbg_0.13-1~nd10.10+1_amd64.deb Size: 1711982 SHA256: 0e22c074117ed14ab06670c17f8657bc20e8b7c881cc7048dbdacb071846be55 SHA1: 9c19b4e7b7e21c3a2c5783a72db0bf2ccb3320c5 MD5sum: 8d5323e51fb18f0fdc14809311b14ae0 Description: C-Extensions for Python (Debug Build of Cython) This package contains Cython libraries built against versions of Python configured with --pydebug. Python-Version: 2.6 Package: debruijn Version: 1.5-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1) Homepage: http://www.cfn.upenn.edu/aguirre/wiki/public:de_bruijn_software Priority: extra Section: science Filename: pool/main/d/debruijn/debruijn_1.5-1~nd10.10+1_amd64.deb Size: 35010 SHA256: 2e88a279635e1b45ecd9cc6428077684d9369be9875800524ce51873b7efd36a SHA1: db27fb264201c82a20fd6f560e77546f714e7c92 MD5sum: 3bba8c88f1e3b00073aa285af9979ae3 Description: De Bruijn cycle generator Stimulus counter-balance is important for many experimental designs. This command-line software creates De Bruijn cycles, which are pseudo-random sequences with arbitrary levels of counterbalance. "Path-guided" de Bruijn cycles may also be created. These sequences encode a hypothesized neural modulation at specified temporal frequencies, and have enhanced detection power for BOLD fMRI experiments. Package: dicomnifti Version: 2.28.16-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 476 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libstdc++6 (>= 4.4.0) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.28.16-1~nd10.10+1_amd64.deb Size: 157922 SHA256: f164063576402d1d34822021d5a9ba4a749fef551ae77d5501a683d596cd4ccc SHA1: 0024a95907ed002ebaa8c8bbac2b5cb5dbc4de5a MD5sum: f6099f793ce57367079220eadec57da5 Description: converts DICOM files into the NIfTI format The dinifti program converts MRI images stored in DICOM format to NIfTI format. The NIfTI format is thought to be the new standard image format for medical imaging and can be used with for example with FSL, AFNI, SPM, Caret or Freesurfer. . dinifti converts single files, but also supports fully automatic batch conversions of complete dicomdirs. Additionally, converted NIfTI files can be properly named, using image series information from the DICOM files. Package: dmtcp Version: 1.2.1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3680 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: utils Filename: pool/main/d/dmtcp/dmtcp_1.2.1-2~nd10.10+1_amd64.deb Size: 1639354 SHA256: 3719897d9eb460594d06520d810d5e7215422651ce3b97ae46a13ac07cbea410 SHA1: 817306635f2047baae023ec45b74c6efd7d079ef MD5sum: 3406f6fbe222c90707950d984ae4ff11 Description: Checkpoint/Restart functionality for Linux processes DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package contains DMTCP binaries. Package: dmtcp-dbg Source: dmtcp Version: 1.2.1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21252 Depends: neurodebian-popularity-contest, dmtcp Homepage: http://dmtcp.sourceforge.net Priority: extra Section: debug Filename: pool/main/d/dmtcp/dmtcp-dbg_1.2.1-2~nd10.10+1_amd64.deb Size: 6383190 SHA256: b3073f66c0c8fa8fa7675e11d76e48d51e1a47db5afc7cd94c7995533f6177f7 SHA1: 75b8de87c1ee80418c4e6cb7070f29f2efad4c7d MD5sum: 9e86d2f8a6fe3e9771f99bb597ac04c0 Description: Debug package for dmtcp DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package contains debugging symbols for DMTCP. Package: eatmydata Source: libeatmydata Version: 26-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5) Provides: libeatmydata Homepage: https://launchpad.net/libeatmydata Priority: optional Section: utils Filename: pool/main/libe/libeatmydata/eatmydata_26-2~nd10.10+1_amd64.deb Size: 8170 SHA256: 68a067f3f6568320b30de47135a05791d0503f9ba4a53118baa2f5acf5c5daeb SHA1: 7f05d2bbae1138d94ba56a988d676e8e726b3543 MD5sum: 77af09647b3176efac4e22bc8dd44c81 Description: library and utilities designed to disable fsync and friends This package contains a small LD_PRELOAD library (libeatmydata) and a couple of helper utilities designed to transparently disable fsync and friends (like open(O_SYNC)). This has two side-effects: making software that writes data safely to disk a lot quicker and making this software no longer crash safe. . You will find eatmydata useful if particular software calls fsync(), sync() etc. frequently but the data it stores is not that valuable to you and you may afford losing it in case of system crash. Data-to-disk synchronization calls are typically very slow on modern file systems and their extensive usage might slow down software significantly. It does not make sense to accept such a hit in performance if data being manipulated is not very important. . On the other hand, do not use eatmydata when you care about what software stores or it manipulates important components of your system. The library is called libEAT-MY-DATA for a reason. Package: fail2ban Version: 0.8.4+svn20110323-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 596 Depends: neurodebian-popularity-contest, python (>= 2.4), python-central (>= 0.6.11), lsb-base (>= 2.0-7) Recommends: iptables, whois Suggests: python-gamin, mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.4+svn20110323-1~nd10.10+1_all.deb Size: 97968 SHA256: f8fb14c62e06cdd270edecbba55185610ee182a284467571086d8bb846ee0ca0 SHA1: ab194e62672c513f43d29d9e2c16823e15b33048 MD5sum: 95b6a2d520b238d58fd25367aced3c7d Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Python-Version: current, >= 2.4 Package: freenect Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libfreenect-demos, libfreenect-dev, libfreenect0.0 Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/freenect_0.0.1+20101211+2-2~nd10.10+1_amd64.deb Size: 2542 SHA256: 300e70f32cf6ef6ff4083c4f1e79991a72e3edbef3db1ed82654b13e2902c381 SHA1: 88d1b71f6fd2502eb4c17bba6dcc0adbf29922dc MD5sum: a42aab40260b0576096fbaa3ae30bcab Description: library for accessing Kinect USB camera -- meta package libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the meta package to install all components of the project. Package: fslview Version: 3.1.8+4.1.6-2~maverick.nd2 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4136 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.4.0), libvtk5.4, libvtk5.4-qt3 Recommends: fslview-doc Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_3.1.8+4.1.6-2~maverick.nd2_amd64.deb Size: 1511708 SHA256: 7617b502940dd1d0dcd8b8d409e2b71554b0fbbf2ef01ed26aa95556b8cf653e SHA1: d9145e95a4887b0a05bc363de080969d7a9f130d MD5sum: 4bc78a17396b1e4798ab64fdeced84f5 Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 3.1.8+4.1.6-2~maverick.nd2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3216 Depends: qt3-assistant Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_3.1.8+4.1.6-2~maverick.nd2_all.deb Size: 2378976 SHA256: f86a19e226efeeb0c052522258b201d7f6c822e4bd9f0e0816a75daa0778f782 SHA1: 7cf0703841a0c9fb0338253f749f6b668671db08 MD5sum: bd2d711aef575e4963a60637ae6ffc0b Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gdf-tools Source: libgdf Version: 0.1.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 116 Depends: neurodebian-popularity-contest, libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-program-options1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: utils Filename: pool/main/libg/libgdf/gdf-tools_0.1.1-1~nd10.10+1_amd64.deb Size: 38832 SHA256: 78a11b37b58b4e6c27f623d8244141bbb738955729d98931cd6b6b4bfd9f0cee SHA1: d822fdf40a703d7253cbef0303c11283d51e798e MD5sum: 8b4faa76b96aea7deeca6897cb1313b0 Description: IO library for the GDF -- helper tools GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the tool shipped with the library (gdf_merger). Package: gifti-bin Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 128 Depends: libc6 (>= 2.3.4), libexpat1 (>= 1.95.8), libgiftiio0, libnifti1 (>> 1.1.0-2), zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: utils Filename: pool/main/g/gifticlib/gifti-bin_1.0.9-1~maverick.nd1_amd64.deb Size: 29248 SHA256: 0e3f17a174bdf3fbc004c4c8b3fea6b1de90d5e745355e9907eecccc3c45aa79 SHA1: 5a1f62520a7089fc0f7779a6aad302eb8e8623f9 MD5sum: 371cc7de70de99fd19a3b4aef2fb0940 Description: tools shipped with the GIFTI library GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the tools that are shipped with the library (gifti_tool and gifti_test). Package: glew-utils Source: glew Version: 1.6.0-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 384 Depends: neurodebian-popularity-contest, libglew1.6 (= 1.6.0-2~nd10.10+1), libc6 (>= 2.4), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libx11-6, libxext6, libxi6, libxmu6 Replaces: libglew1.4 (<< 1.5) Homepage: http://glew.sourceforge.net/ Priority: optional Section: utils Filename: pool/main/g/glew/glew-utils_1.6.0-2~nd10.10+1_amd64.deb Size: 114212 SHA256: bc4b399659cfedaf93ff78f9838aca231c9d0d9ff597fe0dc5f03957ae2b8260 SHA1: 08388f8116895427e4392ce5a60ececb45702248 MD5sum: 04ba2607d0b5ae3edad854c922092382 Description: OpenGL Extension Wrangler - utilities For more information about GLEW please refer to the description of the libglew1.6-dev package. . This package contains the utilities which can be used to query the supported openGL extensions. Package: itksnap Version: 2.2.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8512 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.18, libstdc++6 (>= 4.4.0), libvtk5.4 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.2.0-1~nd10.10+1_amd64.deb Size: 3659988 SHA256: 428cba68d85dfc778c63d498f32e7fa577d242153bb7bf0030d816dab47a7309 SHA1: b8228d7d557e331f97bf4e3d7c62d4c58b09e04c MD5sum: 9a163ff84b7d6e864eb590cd8b9cefd0 Description: semi-automatic segmentation of structures in 3D images SNAP provides semi-automatic segmentation of structures in medical images (e.g. magnetic resonance images of the brain) using active contour methods, as well as manual delineation and image navigation. Noteworthy features are: . * Linked cursor for seamless 3D navigation * Manual segmentation in three orthogonal planes at once * Support for many different 3D image formats, including NIfTI * Support for concurrent, linked viewing and segmentation of multiple images * Limited support for color images (e.g., diffusion tensor maps) * 3D cut-plane tool for fast post-processing of segmentation results Package: klustakwik Version: 2.0.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://sourceforge.net/projects/klustakwik/ Priority: extra Section: science Filename: pool/main/k/klustakwik/klustakwik_2.0.1-1~nd10.10+1_amd64.deb Size: 21462 SHA256: b67c20c13c6bb911bdad2681680d0c52fe4df87794713316972dcb6fd4456283 SHA1: 50437f24ec71448d3a147c4d24e51a64eee7cb03 MD5sum: ce90c8425a56ed4a7f3bfa17c4eb7539 Description: automatic sorting of the samples (spikes) into clusters KlustaKwik is a program for automatic clustering of continuous data into a mixture of Gaussians. The program was originally developed for sorting of neuronal action potentials, but can be applied to any sort of data. Package: libbiosig-dev Source: biosig4c++ Version: 0.96.3+svn2677-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1608 Depends: neurodebian-popularity-contest, libbiosig0 (= 0.96.3+svn2677-1~nd10.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.96.3+svn2677-1~nd10.10+1_amd64.deb Size: 389824 SHA256: 431add5b2a9fe30f5af8c894492467071510f22dd48177b603846f0aab5909c1 SHA1: 01248756ad8aba36b4a57f71d8c9138a8c94c7b0 MD5sum: f140bb53dd481d5dadb12e17bae425ea Description: I/O library for biomedical data - development files BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides header files and static library. Package: libbiosig0 Source: biosig4c++ Version: 0.96.3+svn2677-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 880 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig0_0.96.3+svn2677-1~nd10.10+1_amd64.deb Size: 309662 SHA256: 440d0c6ba877f2db38c821226fdfa37c675709c07acea00d78325f1089f2b410 SHA1: e0d2409745b8340b788e6070ac020c49ec44798e MD5sum: 46881033b715d70fc6ec85781fdc9628 Description: I/O library for biomedical data - dynamic library BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides dynamic library. Package: libbiosig0-dbg Source: biosig4c++ Version: 0.96.3+svn2677-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 220 Depends: neurodebian-popularity-contest, libbiosig0 (= 0.96.3+svn2677-1~nd10.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig0-dbg_0.96.3+svn2677-1~nd10.10+1_amd64.deb Size: 57474 SHA256: 0c4ca86ba651fcef3b416c5881221b5e9744ca0a7b8a9630890e219b025effa3 SHA1: f73bd4b9832ef348d0cd2134de3213c2861ab077 MD5sum: f743fe552b3a4e5d9ab7f55199389218 Description: I/O library for biomedical data - debug symbols BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides debug symbols. Package: libcgroup-dev Source: libcgroup Version: 0.37.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libcgroup1 (= 0.37.1-1~nd10.10+1) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libdevel Filename: pool/main/libc/libcgroup/libcgroup-dev_0.37.1-1~nd10.10+1_amd64.deb Size: 17398 SHA256: 0fb23216158969e4a2093148892441cf56cf464139cf75d4b3d578057dbbbd6a SHA1: a08186c744c2f93674fb8603bbf4208c55fd883c MD5sum: 0acb254711b43e6ede397d0e626579b1 Description: Development libraries to develop applications that utilize control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . It provides API to create/delete and modify cgroup nodes. It will also in the future allow creation of persistent configuration for control groups and provide scripts to manage that configuration. Package: libcgroup1 Source: libcgroup Version: 0.37.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, libc6 (>= 2.8) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libs Filename: pool/main/libc/libcgroup/libcgroup1_0.37.1-1~nd10.10+1_amd64.deb Size: 38254 SHA256: 7b1b80e4a626374212506d191eed9995c89419e10a3b8379930ac5ee6daca8f9 SHA1: faeb224470499c32c508c87905e9e97929b3bee3 MD5sum: e0a2c20eb60012a4e4dd4a5c1cea302d Description: Library to control and monitor control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . This library allows applications to manipulate, control, administrate and monitor control groups and the associated controllers. Package: libclassad-dev Source: classads Version: 1.0.10-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2188 Depends: neurodebian-popularity-contest, libclassad1 (= 1.0.10-2~nd10.10+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libdevel Filename: pool/main/c/classads/libclassad-dev_1.0.10-2~nd10.10+1_amd64.deb Size: 571248 SHA256: 1ac258222ad7bee4066203b3f0c022f41e72be68497c5de88fc510eadb2537c7 SHA1: ec7f1c0895aabce6c19abb811aab9b349b36e711 MD5sum: 26cb9dd127a53571189d5638f83dadcf Description: library for Condor's classads expression language (development) A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the static library and header files. Package: libclassad0 Source: classads Version: 1.0.9-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1060 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libs Filename: pool/main/c/classads/libclassad0_1.0.9-2~nd10.10+1_amd64.deb Size: 420736 SHA256: 4e97eb772a263c3966aaea8bba96b532969e2f9df49fb1d7b0608c5116b2626b SHA1: d3754566b5f39b91eee1447d928760b8c4fb100a MD5sum: 8d00ad8adc4dd66e7e3025075bdc0090 Description: library for Condor's classads expression language A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the runtime library. Package: libclassad1 Source: classads Version: 1.0.10-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1024 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libs Filename: pool/main/c/classads/libclassad1_1.0.10-2~nd10.10+1_amd64.deb Size: 421294 SHA256: 8386410048c31830d2212d02bbe6a3df75926641b6a24b69f792ea9ccbe96736 SHA1: eaf2a7db4dc8823a2f02687d0ea3da6fff208f00 MD5sum: 7f6ab71ce60b34212bb92b53f4c4cb92 Description: library for Condor's classads expression language A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the runtime library. Package: libdmtcpaware-dev Source: dmtcp Version: 1.2.1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, libdmtcpaware1 (= 1.2.1-2~nd10.10+1) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/d/dmtcp/libdmtcpaware-dev_1.2.1-2~nd10.10+1_amd64.deb Size: 6580 SHA256: 751571fdf7c873deca791febb7259fa8ac53e3ed04ed4e3194ff63fc062c51f5 SHA1: 1a59f175c4c07421b17025bb9e4f5348c1a42c80 MD5sum: ea6ea0175c642f0b1cd721d0a3b75544 Description: DMTCP programming interface -- developer package DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides libraries for developing applications that need to interact with dmtcp. Package: libdmtcpaware1 Source: dmtcp Version: 1.2.1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, dmtcp, libc6 (>= 2.2.5) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libdmtcpaware1_1.2.1-2~nd10.10+1_amd64.deb Size: 6374 SHA256: f6bf593af4b9be25caf9fd5d4efe6ee47244c261182d326d35b34f21fd5abe9f SHA1: 0946c0e9d4e0cb6a77f22af33d36dbe649b0ee59 MD5sum: 73d1d8a33e4d599510a3f7e17409d808 Description: DMTCP programming interface DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides a programming interface to allow checkpointed applications to interact with dmtcp. Package: libeigen3-dev Source: eigen3 Version: 3.0.1-1.1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3412 Depends: neurodebian-popularity-contest Suggests: libeigen3-doc Homepage: http://eigen.tuxfamily.org Priority: extra Section: libdevel Filename: pool/main/e/eigen3/libeigen3-dev_3.0.1-1.1~nd10.10+1_amd64.deb Size: 509892 SHA256: d06ef342becbd81bf918b8d2fa5f95787143715e0f2f8db0534ea412838aea89 SHA1: f50f9322bc77fd7ed2190b826009dbb18355b361 MD5sum: 5f99a2cba8833219c825de8e6e74b4ff Description: lightweight C++ template library for linear algebra Eigen 3 is a lightweight C++ template library for vector and matrix math, a.k.a. linear algebra. . Unlike most other linear algebra libraries, Eigen 3 focuses on the simple mathematical needs of applications: games and other OpenGL apps, spreadsheets and other office apps, etc. Eigen 3 is dedicated to providing optimal speed with GCC. A lot of improvements since 2-nd version of Eigen. Package: libeigen3-doc Source: eigen3 Version: 3.0.1-1.1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10224 Depends: neurodebian-popularity-contest, ttf-freefont, libjs-jquery Suggests: libeigen3-dev Homepage: http://eigen.tuxfamily.org Priority: extra Section: doc Filename: pool/main/e/eigen3/libeigen3-doc_3.0.1-1.1~nd10.10+1_all.deb Size: 2326772 SHA256: 3092239d503e816cf4dcebf28d1404ed1b2ef479a0fd881524ab3dbb7b0204da SHA1: 9c1ddfcabbea72f076791e710338e47db69f1cee MD5sum: c594fa03fab1448d72e07f73d818bcf3 Description: eigen3 API docmentation Eigen 3 is a lightweight C++ template library for vector and matrix math, a.k.a. linear algebra. . This package provides the complete eigen3 API documentation in HTML format. Package: libfreenect-demos Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 92 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.3.4), libfreenect0.0, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libice6 (>= 1:1.0.0), libsm6, libstdc++6 (>= 4.4.0), libusb-1.0-0 (>= 2:1.0.8), libx11-6, libxext6, libxi6, libxmu6, libglut3 Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-demos_0.0.1+20101211+2-2~nd10.10+1_amd64.deb Size: 25284 SHA256: df26e163ffd40605e90adfb697d975086011a19b955a3d18881f7b92eb97444b SHA1: 663275c3d02e1b71750e4487dfc67cc34e1238fb MD5sum: e09f6ea2e8b077835e29a428bda0741f Description: library for accessing Kinect USB camera -- demonstrations libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package includes some example programs for kinect. All programs start with a freenec- prefix. Package: libfreenect-dev Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, libusb-1.0-0-dev, libfreenect0.0 (= 1:0.0.1+20101211+2-2~nd10.10+1) Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-dev_0.0.1+20101211+2-2~nd10.10+1_amd64.deb Size: 18462 SHA256: 25a61fa4843dc12d498f1af9ad54bf2edafb47fe4e08b3c91528c83754090144 SHA1: 2277eb3d8c45c0910284cc493470aabd7e9de51d MD5sum: 52d52dee4efc245343c4e557901cad70 Description: library for accessing Kinect USB camera -- development files libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the development package containing the libraries and header for software development with libfreenect. Package: libfreenect0.0 Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libusb-1.0-0 (>= 2:1.0.8), udev Conflicts: libfreenect Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.0_0.0.1+20101211+2-2~nd10.10+1_amd64.deb Size: 27760 SHA256: 4cc34beeb31aacdfb9c2dfcc5349b1c7532cf2699be5f9d32bf26626664b947c SHA1: bd0819346ab0712fcd38a1b40a86c554e242a43b MD5sum: 2d7132bcf03d73876d6e084ce30840db Description: library for accessing Kinect USB camera libfreenect is the core library for accessing the Microsoft Kinect USB camera. Currently, the library supports access to: - RGB and Depth Images - Motors - Accelerometer - LED Package: libgdf-dev Source: libgdf Version: 0.1.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 140 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1-1~nd10.10+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libdevel Filename: pool/main/libg/libgdf/libgdf-dev_0.1.1-1~nd10.10+1_amd64.deb Size: 18532 SHA256: 2defff266e8bb2caa607e5f62111107b9f7efcff23469b580381c5f50ac603f7 SHA1: e9e55b7504b82fa93f9674359865bd1bbedeb70d MD5sum: 59cb50c2ba4692d0e720a4f8cbbd324a Description: IO library for the GDF -- development library GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the header files and static library. Package: libgdf0 Source: libgdf Version: 0.1.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 328 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libs Filename: pool/main/libg/libgdf/libgdf0_0.1.1-1~nd10.10+1_amd64.deb Size: 103042 SHA256: a29cf9efb120fc91a0d6f0d6cad0398b7d59c2b1d497ca6b060c89e379b74129 SHA1: 5aaa4c1c873585dc1aeb4bd6f81d22b659303389 MD5sum: cba1d457d04acfebe16b9e2d301cba5a Description: IO library for the GDF (general dataformat for biosignals) GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package contains the shared library. Package: libgdf0-dbg Source: libgdf Version: 0.1.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4108 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1-1~nd10.10+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: debug Filename: pool/main/libg/libgdf/libgdf0-dbg_0.1.1-1~nd10.10+1_amd64.deb Size: 1119386 SHA256: f55652c057990c21f32a016b343388f58439f3f7033e9ff728cecb44571e2765 SHA1: 64aff3737fbc50c9b76598178fa3b6e73e283c19 MD5sum: 931b7dd23f9bb7b3365ad67139f1c466 Description: IO library for the GDF -- debug symbols GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides debug symbols. Package: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 256 Depends: libgiftiio0 (= 1.0.9-1~maverick.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~maverick.nd1_amd64.deb Size: 65020 SHA256: a5e63ac7d35ef813eed1d81d98075530acbe2f058a945bd212566b4d468bcfc2 SHA1: 47c3332fe15460f79c90c5ca42a2059c93e8e2e9 MD5sum: 6861952fd85b1efebfc95d6a120d9efb Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the header files and static library. Package: libgiftiio0 Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 180 Depends: libc6 (>= 2.4), libexpat1 (>= 1.95.8), libnifti1 (>> 1.1.0-2), zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libs Filename: pool/main/g/gifticlib/libgiftiio0_1.0.9-1~maverick.nd1_amd64.deb Size: 57604 SHA256: e4754cde9e0089b5774f5bbd3739171f5a68a0115cd50fd84a8c9aa1035fbfcb SHA1: 11983a5c86ae4fab0008551c4f17a5cc0d7ccb1e MD5sum: e7eacce11ea15f9b8b97ea6a242a36c0 Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package contains the shared library. Package: libglew1.6 Source: glew Version: 1.6.0-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 424 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libx11-6, libxext6, libxi6, libxmu6 Suggests: glew-utils Conflicts: libglew1 Homepage: http://glew.sourceforge.net/ Priority: optional Section: libs Filename: pool/main/g/glew/libglew1.6_1.6.0-2~nd10.10+1_amd64.deb Size: 112998 SHA256: b5b9bb4ffd4a855d81ddeb945b39f38ccc8b0acdd429dff7d63a4c401399b50c SHA1: d44f53fbaa44f3d6db9f178f494da8c10f83175f MD5sum: f37d43a4a51dca0781a9481660d501cb Description: OpenGL Extension Wrangler - runtime environment For more information about GLEW please refer to the description of the libglew-dev package. . This package contains the runtime support files. Package: libglew1.6-dev Source: glew Version: 1.6.0-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1492 Depends: neurodebian-popularity-contest, libglew1.6 (= 1.6.0-2~nd10.10+1), libgl1-mesa-dev | libgl-dev, libglu1-mesa-dev | libglu-dev Conflicts: libglew-dev, libglew1.5-dev Provides: libglew-dev, libglew1.5-dev Homepage: http://glew.sourceforge.net/ Priority: optional Section: libdevel Filename: pool/main/g/glew/libglew1.6-dev_1.6.0-2~nd10.10+1_amd64.deb Size: 238436 SHA256: d5081d22961909e0e8e2a2ed72270a8a46988a7ff4cbcdc067d122e74fcf3cd0 SHA1: bf00d3f31601b33acabaa6051da8fb94ff061813 MD5sum: 7a8fb6b3c429dacd222a724447bca0c8 Description: OpenGL Extension Wrangler - development environment The OpenGL Extension Wrangler, GLEW for short, is a library that handles initialization of OpenGL extensions in a portable and simple way. Once the program initializes the library and checks the availability of extensions, it can safely call the entry points defined by the extension. Currently GLEW supports almost all the extensions found in the OpenGL extension registry[1]. . This package contains the development documentation as well as the required header files. Package: libglewmx1.6 Source: glew Version: 1.6.0-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 376 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libx11-6, libxext6, libxi6, libxmu6 Conflicts: libglew1 Homepage: http://glew.sourceforge.net/ Priority: optional Section: libs Filename: pool/main/g/glew/libglewmx1.6_1.6.0-2~nd10.10+1_amd64.deb Size: 101116 SHA256: dffe4554abd9c99bc9b84b35211b125bfa4321073d9fa8269c2a10240ac52e01 SHA1: 29578e6fdfddc815db1f6679bcfa8577a34f5880 MD5sum: 4a26eab9f56a79a355b235a934a119d2 Description: OpenGL Extension Wrangler - runtime environment For more information about GLEW please refer to the description of the libglew-dev package. . This package contains the runtime support files, built with GLEW_MX option, adding support for thread-safe usage of multiple rendering contexts. Package: libglewmx1.6-dev Source: glew Version: 1.6.0-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 512 Depends: neurodebian-popularity-contest, libglew-dev, libglewmx1.6 (= 1.6.0-2~nd10.10+1) Conflicts: libglewmx-dev, libglewmx1.5-dev Provides: libglewmx-dev, libglewmx1.5-dev Homepage: http://glew.sourceforge.net/ Priority: optional Section: libdevel Filename: pool/main/g/glew/libglewmx1.6-dev_1.6.0-2~nd10.10+1_amd64.deb Size: 96706 SHA256: 693666270795a791a423392d735ce48432a63332561d657a7953ac11a357c4f1 SHA1: 5e3687859c67cb80f007b1eaec83ae09d14b8abb MD5sum: 92e09ff34215659b645221a209bf5e07 Description: OpenGL Extension Wrangler - development environment The OpenGL Extension Wrangler, GLEW for short, is a library that handles initialization of OpenGL extensions in a portable and simple way. Once the program initializes the library and checks the availability of extensions, it can safely call the entry points defined by the extension. Currently GLEW supports almost all the extensions found in the OpenGL extension registry[1]. . This package contains the development libraries compiled with GLEW_MX Package: libnifti-dev Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 624 Depends: libnifti2 (= 2.0.0-1~maverick.nd1) Conflicts: libfslio-dev, libnifti0-dev, libnifti1-dev, libniftiio-dev Replaces: libnifti1-dev Homepage: http://niftilib.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/n/nifticlib/libnifti-dev_2.0.0-1~maverick.nd1_amd64.deb Size: 171296 SHA256: a10c25e55440328b1c4ac917735541ee5640176d41cc940c8c6564718d728bf6 SHA1: f0df2a274d65f9c6725a2769503bbbfbb2848624 MD5sum: fc52429aa09b32657fe7d09d3bc8ed11 Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the header files and static libraries of libniftiio, znzlib and libnifticdf. Package: libnifti-doc Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1896 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~maverick.nd1_all.deb Size: 245398 SHA256: ee170c249d6406abcf28bf82179b9327507e20bc341314191e9339ddbdf725f5 SHA1: 255d82f6d5115000ec47d28dd096cea340261cfc MD5sum: 6293a28d6b4ab6f3e08ee6f766fdd757 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libnifti2 Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 336 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-1~maverick.nd1_amd64.deb Size: 123028 SHA256: 6dc383c542f510dfe43d769101d51d117497011604bbe6dedd2dc2a2ea5e59e1 SHA1: 7e51b50bb5c2e73003a779dcb5818de3ded222d6 MD5sum: c00979d1767d6b98c166fea6528485b8 Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package contains the shared library of the low-level IO library niftiio, low-level IO library znzlib and the nifticdf shared library that provides functions to compute cumulative distributions and their inverses. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 276 Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 43850 SHA256: c65760742eb06e6a12fd758b5db143564e27dd25b2eff3f98854e035566991f0 SHA1: 0175dc1e5a5701a201119d7901e256882549e439 MD5sum: 6c45ad3f49003ead9939df00c72abada Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides static libraries and header files. Package: libopenmeeg1 Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 960 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/libopenmeeg1_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 252568 SHA256: 87d6e3f667c493b8615eadbf411b841fde7c47364991ee9256b7e13479a90175 SHA1: 13b3b2307374b46e0421791084bae02fed18238e MD5sum: 30c910d0f9bda2c9d2a4b47cc5812920 Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. Package: libopenwalnut1 Source: openwalnut Version: 1.2.5-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5264 Depends: neurodebian-popularity-contest, ttf-liberation (>= 1.0.0), libboost-date-time1.42.0 (>= 1.42.0-1), libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-regex1.42.0 (>= 1.42.0-1), libboost-signals1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libboost-thread1.42.0 (>= 1.42.0-1), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libopenscenegraph65 (>= 2.8.3), libstdc++6 (>= 4.4.0) Suggests: nvidia-glx | fglrx-glx Homepage: http://www.openwalnut.org Priority: extra Section: libs Filename: pool/main/o/openwalnut/libopenwalnut1_1.2.5-1~nd10.10+1_amd64.deb Size: 1630078 SHA256: 500411bde43331f11bf01ab3e9dbb86eb05fc5b9a5d2556ff9af1fc1ba544b0b SHA1: d5eae6a2335f3818c637b5d695bc5409a2fd3773 MD5sum: a50509b4343676854251330bb9e5f413 Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API of OpenWalnut. Package: libopenwalnut1-dev Source: openwalnut Version: 1.2.5-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2092 Depends: neurodebian-popularity-contest, libopenwalnut1 (= 1.2.5-1~nd10.10+1), libgl1-mesa-dev | libgl-dev, libopenscenegraph-dev (>= 2.8.1), libopenthreads-dev (>= 2.8.1), libboost-dev (>= 1.42.0), libboost-program-options-dev (>= 1.42.0), libboost-thread-dev (>= 1.42.0), libboost-filesystem-dev (>= 1.42.0), libboost-date-time-dev (>= 1.42.0), libboost-system-dev (>= 1.42.0), libboost-signals-dev (>= 1.42.0), libboost-regex-dev (>= 1.42.0), libeigen3-dev (>= 3.0.0) Homepage: http://www.openwalnut.org Priority: extra Section: libdevel Filename: pool/main/o/openwalnut/libopenwalnut1-dev_1.2.5-1~nd10.10+1_amd64.deb Size: 262310 SHA256: f8578863386d966d96659faff5d58f0bf7cd9b5c04323d0b6cb0ede0f995667b SHA1: d5e5e05c2436994fcf8bc76e783100a11a004fff MD5sum: d080e0513b8f2613fc647e7e94d7044c Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the headers for the core API of OpenWalnut. Package: libopenwalnut1-doc Source: openwalnut Version: 1.2.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41232 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.2.5-1~nd10.10+1_all.deb Size: 4250874 SHA256: 589465429b4b2fa9bef380c565660c8bc6d1f92834f1f66979871643eca344fd SHA1: 2fbf86d74923569f6113c456e0f74e24f32dcf8e MD5sum: 795c73647a2947729dd967f0eb076884 Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: libpam-cgroup Source: libcgroup Version: 0.37.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libcgroup1, libpam0g (>= 0.99.7.1) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: admin Filename: pool/main/libc/libcgroup/libpam-cgroup_0.37.1-1~nd10.10+1_amd64.deb Size: 7726 SHA256: 6e218100a7c9763ae66e1076cf8f594ae0409cbc9bff71fc779aaed0a9c140d5 SHA1: 32d3c59b84e29d6649553df85c5624f20b276def MD5sum: 5dc85fa972c9adb1c3382808f7d0343f Description: PAM module to move a user session into a cgroup Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . This PAM module will move a user session into an existing cgroup by attempting to match uid and gid against the defined cgroup rules configuration. Package: libsvm-dev Source: libsvm Version: 3.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd10.10+1) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: libdevel Filename: pool/main/libs/libsvm/libsvm-dev_3.0-1~nd10.10+1_amd64.deb Size: 28200 SHA256: 193d031c0667c81c1eab7c655db8aa82bc895d0f538ea78e9738faf2c5af9cb6 SHA1: a70f27e948c0c278ef67f0be99f8e76c5613dad6 MD5sum: 6d257eaec29bce115e3c9fe9775d3b16 Description: The LIBSVM header files These are the header files for LIBSVM, a machine-learning library. Package: libsvm-java Source: libsvm Version: 3.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libsvm3-java Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm-java_3.0-1~nd10.10+1_all.deb Size: 2068 SHA256: 8c9fbe247ddc6f15bd3e3c64bfd7f1e3c69405f907da4fa7edeadfe05e9d21d8 SHA1: 8053cf3d05442da7571a056b726ac00a3d77b0e1 MD5sum: 14c1bd18250e02bbcd73cfc70bc8be5c Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. . This package solely provides a symbolic link from svm.jar svm3.jar. Package: libsvm-tools Source: libsvm Version: 3.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 344 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python, gnuplot Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: devel Filename: pool/main/libs/libsvm/libsvm-tools_3.0-1~nd10.10+1_amd64.deb Size: 119876 SHA256: 24047b90f8ca21718b3c0e18b8ff35776f428a237e4339e9b737b6c71e5d0088 SHA1: ef009d9a0cc16aa59dd2d82f591307907a650964 MD5sum: 066e87449c23ca64e084440146e7f239 Description: The LIBSVM binary tools LIBSVM is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection. LIBSVM homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm Package: libsvm3 Source: libsvm Version: 3.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 132 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: libs Filename: pool/main/libs/libsvm/libsvm3_3.0-1~nd10.10+1_amd64.deb Size: 46226 SHA256: 578ce97fc2b26f01d354346e6d3d506ca96a57d8c2e3f32e9fe9b9633f1feadb SHA1: 50c1135ef4eda1297908a6a51f5f7ff9ac6fdc63 MD5sum: 7ebafe30e30b61295b29d5c8b2b6f576 Description: library implementing support vector machines The LIBSVM library is used to calculate Support Vector Machine optimizations with a great variety of powerful options. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. It is primarily of interest to machine-learning researchers and artificial intelligence application developers. Package: libsvm3-java Source: libsvm Version: 3.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, gij | java-gcj-compat | java1-runtime | java2-runtime Suggests: java-virtual-machine Conflicts: libsvm2-java (<= 2.91-2) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm3-java_3.0-1~nd10.10+1_all.deb Size: 60484 SHA256: 76811edf43b1aefb334ae653f274201c297bcccac8ac8530bec925b93dff1492 SHA1: 7e6ee936ff6b7679bd9f97fccede3700997e4f37 MD5sum: 6f1946dff229612af5767fcf8da60091 Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. Package: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 3940 Depends: libc6 (>= 2.7), libdcmtk1 (>= 3.5.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libnifti1 (>> 1.1.0-2), libqt3-mt (>= 3:3.3.8-b), libsm6, libstdc++6 (>= 4.4.0), libvia0, libx11-6, libxext6, zlib1g (>= 1:1.1.4), via-bin Recommends: dcmtk, lipsia-doc Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: science Filename: pool/main/l/lipsia/lipsia_1.6.0-4~maverick.nd1_amd64.deb Size: 1350080 SHA256: d6e5814498a44d6f9aa68c26dfe86e5a24398c6727efb40240ab872fad14e217 SHA1: 2887d9c808cbc0861958043d8dea9eee1ccc9c3a MD5sum: 109c0c9b499315d58dad5395c035eb9d Description: analysis suite for MRI and fMRI data Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This is a software package for the data processing and evaluation of functional magnetic resonance images. The analysis of fMRI data comprises various aspects including filtering, spatial transformation, statistical evaluation as well as segmentation and visualization. All these aspects are covered by LIPSIA. For the statistical evaluation, a number of well established and peer-reviewed algorithms were implemented in LIPSIA that allow an efficient and user-friendly processing of fMRI data sets. As the amount of data that must be handled is enormous, an important aspect in the development of LIPSIA was the efficiency of the software implementation. . LIPSIA operates exclusively on data in the VISTA data format. However, the package contains converters for medical image data in iBruker, ANALYZE and NIfTI format -- converting VISTA images into NIfTI files is also supported. Package: lipsia-doc Source: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 7004 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/l/lipsia/lipsia-doc_1.6.0-4~maverick.nd1_all.deb Size: 5539260 SHA256: 82338bf6b7642c81e3d25791b543695be0cfdce051003635d2635dd36680651e SHA1: 4605c3847915a816361fb32d35551861a05db84d MD5sum: 405aaaf745427fdfe430d25bb7e8b238 Description: documentation for LIPSIA Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This package provides the LIPSIA documentation in HTML format. Package: matlab-support-dev Source: matlab-support Version: 0.0.16~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.16~nd10.10+1_all.deb Size: 6022 SHA256: aebaa4526f3eec31dbfaa8e9eb9e9b7510eb11c8523c8c970691ed6461399d3c SHA1: 0c51e69e4169037170ae22d22148310ebabe9a3b MD5sum: d658d0d72da77c35b4508d4f77961ab4 Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mriconvert Version: 2.0.217-3~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2160 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.11.0), libwxgtk2.8-0 (>= 2.8.11.0) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.217-3~nd10.10+1_amd64.deb Size: 767400 SHA256: e3b96fff61075611b9fdd90381be4cb0c70933ce608a0a748cd32fd6fefe7654 SHA1: 3228231b46090ef26b3fd80b0b756b27aa5fb947 MD5sum: 45bcd59f3bd3d7f745e155d97f67cad1 Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5, SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: mricron Version: 0.20110413.1~dfsg.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15632 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.29.3), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.21.6), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.8.0), libpango1.0-0 (>= 1.14.0), libx11-6, mricron-data Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20110413.1~dfsg.1-1~nd10.10+1_amd64.deb Size: 4901244 SHA256: 64a2b8d9f7db3ba2304cc3e28377ba5b13176a2f8c0a552def61f65dbd4a1074 SHA1: 315d26a6994f9b3b06f15f1c4960d748756319f9 MD5sum: 3aefe6360dcbcd7c314a8ddd6bbe0266 Description: magnetic resonance image conversion, viewing and analysis This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20110413.1~dfsg.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1804 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20110413.1~dfsg.1-1~nd10.10+1_all.deb Size: 1663610 SHA256: af4ecf44142487a2f7a706160681667d7fab75fe5ef557681a34350aa3095e5a SHA1: 7db9c822451a73c2861d74ed54592a441ffe9258 MD5sum: 9ee4065d31f95100bd89b11f8aedfff2 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20110413.1~dfsg.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1176 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20110413.1~dfsg.1-1~nd10.10+1_all.deb Size: 735448 SHA256: 85b4b912d0709dad89be8cea868e9d9a882556c6defed39f7a52d5b09d8d3615 SHA1: c14c916d7a1cc8282712c10c7de0e905e5b38a7f MD5sum: b2f69ea8b6f028f7c0603911718cac74 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix Version: 0.2.9-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7052 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.29.3), libc6 (>= 2.4), libcairo2 (>= 1.2.4), libcairomm-1.0-1 (>= 1.6.4), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.21.6), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.25.4), libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1c2a (>= 1:2.20.0), libice6 (>= 1:1.0.0), libpango1.0-0 (>= 1.14.0), libpangomm-1.4-1 (>= 2.26.0), libpng12-0 (>= 1.2.13-4), libsigc++-2.0-0c2a (>= 2.0.2), libsm6, libstdc++6 (>= 4.3), libx11-6, libxmu6, libxt6 Suggests: mrtrix-doc Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: science Filename: pool/main/m/mrtrix/mrtrix_0.2.9-1~nd10.10+1_amd64.deb Size: 2227816 SHA256: 1c80678080edf114169de5ee54932182636ca4d256d96366739c8b14a84282a8 SHA1: 051f8a3ad39ae319e68a16c612f09d3cb1120922 MD5sum: 518cc81e50b0bbaf5b080c814eb7e622 Description: diffusion-weighted MRI white matter tractography Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. Package: mrtrix-doc Source: mrtrix Version: 0.2.9-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3304 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.9-1~nd10.10+1_all.deb Size: 2939866 SHA256: da7dc6bc5c9ece35cf47d742fb0b6532c9b08cb9f62ff6db045d01bfaca39c9a SHA1: fd8105f96bfb67fec48f583640673e4223ca1fc2 MD5sum: 52828b31475013b2ba2f23ebc6a88808 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.25~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 268 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.25~nd10.10+1_all.deb Size: 113502 SHA256: 70af92c2e99532f2841de7cbe428e025c81cc32cf22fb736998b6b7312c720e4 SHA1: fa1e04c93dc426a9de806e22e9bfa792a29ba660 MD5sum: ba74e0caab31fa9cbb3fd3292be03183 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.25~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4408 Depends: devscripts, cowbuilder, python, neurodebian-keyring Recommends: virtualbox-ose, virtualbox-ose-fuse, zerofree, moreutils Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.25~nd10.10+1_all.deb Size: 3845826 SHA256: 09181042da08e6c4e32a50e8ec676a0ae498de341bb5d6fdfacdbe18e05ada90 SHA1: 7640b227f5857ec7cc23aa28f5098a73d8f32c96 MD5sum: 39409f4f872a103fbcb4617bdc3ac522 Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.25~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm, update-manager-gnome, update-notifier Recommends: chromium-browser Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.25~nd10.10+1_all.deb Size: 12832 SHA256: 61f2d4b91844b6f55d3d4b496db83ec64b4337dff239a3fa5e062079e3997437 SHA1: 4c8b3010310072b4e640cf247bef2c3f83acb40f MD5sum: 9860c67f115b4e5ea8b99fb67ae7eedf Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.25~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.25~nd10.10+1_all.deb Size: 5874 SHA256: 12bd04b5505586ce15da89fedaca949a1cab4db325d751c7447784c66a3a0197 SHA1: 612d59fe60aab8ee7feb1ce1ceb314290bc868c4 MD5sum: 199a94d88866b51441f46250a4deb7a4 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.25~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.25~nd10.10+1_all.deb Size: 5036 SHA256: 000e584f76cfa93a889654bf05ab06a66816181a33b9a98788071611b40c8c71 SHA1: 813f7df5f58c4daf9d3e497eb4450a9368de0416 MD5sum: 2e9145691ba2f7ac904460c484384097 Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti-bin Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 200 Depends: libc6 (>= 2.7), libnifti2 Homepage: http://niftilib.sourceforge.net Priority: optional Section: utils Filename: pool/main/n/nifticlib/nifti-bin_2.0.0-1~maverick.nd1_amd64.deb Size: 62302 SHA256: e74b6f17efa9e03e859a450192fe35c0be66c33552d59123271c6704f169758a SHA1: b43f633e1de2af8b1463260231d9e49d819776ef MD5sum: 7947b46fb4d71466cde1e787e1b4f51a Description: tools shipped with the NIfTI library Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test). Package: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 36 Depends: python-nibabel (>= 1.0.0), python-nipy (>= 0.1.2+20110114), python-dipy (>= 0.5.0), python-nipype (>= 0.3.3), python-nitime (>= 0.2) Suggests: python-mvpa, psychopy Homepage: http://www.nipy.org Priority: extra Section: python Filename: pool/main/n/nipy-suite/nipy-suite_0.1.0-2_all.deb Size: 3898 SHA256: 882c8580ebd2d458a92f8d851d1ec9291fecf05f6ed98a8b754eb831c95368c8 SHA1: 6501d1d201160520f5aad29d0f9007c17b7d9778 MD5sum: eb090e568264d2f439892bcb98485b8c Description: Neuroimaging in Python NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. nipy-suite is a metapackage depending on the projects developed under NiPy project umbrella, such as - nibabel: bindings to various neuroimaging data formats - nipy: analysis of structural and functional neuroimaging data - nitime: timeseries analysis - dipy: analysis of MR diffusion imaging data - nipype: pipelines and worfklows Package: nipy-suite-doc Source: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 32 Depends: python-nibabel-doc (>= 1.0.0), python-nipy-doc (>= 0.1.2+20110114), python-dipy-doc (>= 0.5.0), python-nipype-doc (>= 0.3.3), python-nitime-doc (>= 0.2) Suggests: python-mvpa-doc Homepage: http://www.nipy.org Priority: extra Section: doc Filename: pool/main/n/nipy-suite/nipy-suite-doc_0.1.0-2_all.deb Size: 2250 SHA256: 54985bd9d6eaa352608b357f2deeb066bd2ac12d3c2e463082f5d9178701bbad SHA1: 5d2f5e94ff6b7ff737fe966f4a2e5ff67df93cca MD5sum: 37d2f8b6b6d203edf208afb0cdb56fa3 Description: Neuroimaging in Python -- documentation NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. . nipy-suite-doc is a metapackage depending on the documentation packages for NiPy projects. Package: octave-biosig Source: biosig4c++ Version: 0.96.3+svn2677-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 56 Depends: neurodebian-popularity-contest, libbiosig0, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libhdf5-serial-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.7+20100313), libreadline6 (>= 6.0), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_0.96.3+svn2677-1~nd10.10+1_amd64.deb Size: 17748 SHA256: 8b3081cce1d29eff4cb44d0d4924c2fca255c40d87c7969bc5aaa349531cfe80 SHA1: 9cca2d2b9ecab901252c0da64744def240913b02 MD5sum: 4e5100e59858682a3880fe20bac46a79 Description: Octave bindings for BioSig library This package provides Octave bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: octave-gdf Source: libgdf Version: 0.1.1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 328 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.4), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: science Filename: pool/main/libg/libgdf/octave-gdf_0.1.1-1~nd10.10+1_amd64.deb Size: 132960 SHA256: b6ebf8485c16c15b1aa056e2d7b716134ea8ef55c065c20410fd2b74031d5a72 SHA1: ad873fc7d4f83379ae8275764a593a6533750e7d MD5sum: 0ed0591879a9814a7b18b2c0f677311e Description: IO library for the GDF -- Octave interface GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides Octave bindings for libgdf. Package: octave-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2328 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.4), freeglut3, libasound2 (>> 1.0.22), libc6 (>= 2.7), libdc1394-22, libfreenect0.0, libgl1-mesa-glx | libgl1, libglew1.6 (>= 1.6.0), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.24), libopenal1, libpciaccess0 (>= 0.8.0+git20071002), libraw1394-11, libstdc++6 (>= 4.1.1), libusb-1.0-0 (>= 2:1.0.8), libx11-6 (>= 2:1.2.99.901), libxext6, libxi6 (>= 2:1.2.99.4), libxml2 (>= 2.6.27), libxxf86vm1, psychtoolbox-3-common (= 3.0.9+svn2265.dfsg1-1~nd10.10+1), psychtoolbox-3-lib (= 3.0.9+svn2265.dfsg1-1~nd10.10+1) Recommends: octave-audio, octave-image, octave-optim, octave-signal, octave-statistics Provides: psychtoolbox, psychtoolbox-3 Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.9+svn2265.dfsg1-1~nd10.10+1_amd64.deb Size: 743942 SHA256: ce87ce2dc2021574fd5b8f7f4641c226605ad5951c5a38fcfd5ddb43c02f5722 SHA1: cbfa86411829a18a1adf73f03e15b773f8e34edc MD5sum: 8d8406f02ca7948a8d964ebddd731146 Description: toolbox for vision research -- Octave bindings Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains bindings for Octave. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/openmeeg-tools_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 163230 SHA256: fad9d47731fc75238a1a6da647637e110faa81579ba4ea54e5cd1d1e2423a911 SHA1: 724af280bad2953af20b8db0f737b6dde84908dc MD5sum: cde12d4b4d0cd81fb8f41175c72a78df Description: tools for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides command line tools. Package: opensesame Version: 0.24-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5536 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-tk Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.24-1~nd10.10+1_all.deb Size: 3576222 SHA256: 32ec2a336f9577b15293d248763422da8708c73a33af737814b8cd2093c1283f SHA1: 2d2d849b4a3b9f048cd6f88c50d76ea152a6275b MD5sum: d77afa1908eb58d59338ae2ef43cb9aa Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Python-Version: 2.6 Package: openwalnut-modules Source: openwalnut Version: 1.2.5-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 14768 Depends: neurodebian-popularity-contest, libbiosig0, libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-regex1.42.0 (>= 1.42.0-1), libboost-signals1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libboost-thread1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libopenscenegraph65 (>= 2.8.3), libopenwalnut1, libstdc++6 (>= 4.4.0) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-modules_1.2.5-1~nd10.10+1_amd64.deb Size: 4518610 SHA256: 8e5c796bdcc1ce103dcba23450265a372357927800a40f6dea3cff839be73bed SHA1: 1fd51eed5703c3358030b62b5c31714aa3ae3360 MD5sum: 73a80b60effcac3fc00f385601bcc3fb Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the currently available modules for OpenWalnut. Package: openwalnut-qt4 Source: openwalnut Version: 1.2.5-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1868 Depends: neurodebian-popularity-contest, libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-program-options1.42.0 (>= 1.42.0-1), libboost-regex1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libboost-thread1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libopenscenegraph65 (>= 2.8.3), libopenwalnut1, libqt4-opengl (>= 4:4.6.0), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.0), libqtwebkit4 (>= 2.0.0), libstdc++6 (>= 4.4.0) Recommends: openwalnut-modules (= 1.2.5-1~nd10.10+1) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-qt4_1.2.5-1~nd10.10+1_amd64.deb Size: 613518 SHA256: 996071993979ffcb088d9989f7fbb0bf267fca18e10156c442760dcfaa197a59 SHA1: d1f2accefdeaab92cde7cf45c3ddd99cf0492be7 MD5sum: 7e2e4abe5ab8f0d129a3933513ebf449 Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the QT4 GUI for OpenWalnut. Package: packaging-tutorial Version: 0.2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 836 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.2~nd+1_all.deb Size: 680180 SHA256: 4f1c39bc3f108a98284df69fc1734c3eb97957be6e441928596ccbf1ba0d1292 SHA1: 1117c3ff4a15d620a0d8fc62edcc1bfcf45e6329 MD5sum: a3c8023d51d265c6a801496c98620528 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.71.00.dfsg-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5004 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, libavbin0, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.71.00.dfsg-1~nd10.10+1_all.deb Size: 2655686 SHA256: ba1080603b4d7bc01e882cf5f3a90ecd937b51c1c2199ec12962253e83247ebd SHA1: 4a983e6d267434af95758d7373ff2570f9be092f MD5sum: 04fec20700bc7b1cc2fba2f0a4ef67ab Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 54300 Depends: neurodebian-popularity-contest Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.9+svn2265.dfsg1-1~nd10.10+1_all.deb Size: 19675248 SHA256: 32b580244104e3ea5299e4da1a8bbc15f5bdbaa6e8ffa2d094dc6472c39d3898 SHA1: b02614537d0a2cb5605afc2fa8ca4a7624c6e426 MD5sum: 0a6c54108c06c5550cf8b06a870a0da3 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: psychtoolbox-3-dbg Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2412 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.9+svn2265.dfsg1-1~nd10.10+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.9+svn2265.dfsg1-1~nd10.10+1_amd64.deb Size: 753178 SHA256: 290405af835d2f3e8df9b6ec54b7f12cafcc4d3a3bff0e87de3a582e11748fc5 SHA1: fcff7a415c17dfbf0ce99611d5adf8d925142137 MD5sum: eaec510a4aad09a0187a94892efa66eb Description: toolbox for vision research -- debug symbols for binaries Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . To ease debugging and troubleshooting this package contains debug symbols for Octave bindings and other binaries. Package: psychtoolbox-3-lib Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 208 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.1.1) Recommends: gstreamer0.10-plugins-base, gstreamer0.10-plugins-good Suggests: gstreamer0.10-plugins-bad, gstreamer0.10-plugins-ugly Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.9+svn2265.dfsg1-1~nd10.10+1_amd64.deb Size: 60776 SHA256: 7dd0670eddaff1f33798db0a5fdf14126211a4204449eefa58ea192d4797eb6a SHA1: eee89234051505e2c5c9ca100b3bac51f720c53e MD5sum: a5ca2aa350676e8c9797c029b9d0cdc3 Description: toolbox for vision research -- arch-specific parts Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains additional binaries (tools/dynamic libraries) used by both Octave and Matlab frontends. Package: python-biosig Source: biosig4c++ Version: 0.96.3+svn2677-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 220 Depends: neurodebian-popularity-contest, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libbiosig0, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_0.96.3+svn2677-1~nd10.10+1_amd64.deb Size: 52098 SHA256: 6afd915c22defb197d618410ff61b6c6406e03dcd0543c72cade91cb5344b9c6 SHA1: c037b277e6c684a87a1326640292f6fca1867df7 MD5sum: d7395403d5ab977ec98bc1065b615aec Description: Python bindings for BioSig library This package provides Python bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: python-brian Source: brian Version: 1.3.0-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1692 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.3.0-2~nd10.10+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.3.0-2~nd10.10+1_all.deb Size: 314078 SHA256: 69f7314dfa3bc518e81cbf6b137dfa61c5847ca6cf772b5c762fd39511711c8f SHA1: 7afecfd99c984b936ae2bdc49d5dbe4bee216b28 MD5sum: 8bfd788f9ea32cdcc864621164a2a1d4 Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.3.0-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5316 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.3.0-2~nd10.10+1_all.deb Size: 1648440 SHA256: e9c50c73ed839f1d298e462e35e62fbaa1aaa009e290f20a0f375df79dd160fb SHA1: e753e4810a1fe0dd4845685e6968f749fd0b5c6f MD5sum: 3b4fccceb69f41493869aed073c3c46f Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-brian-lib Source: brian Version: 1.3.0-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 156 Depends: neurodebian-popularity-contest, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.3.0-2~nd10.10+1_amd64.deb Size: 53644 SHA256: d90ca00ecdcb16236ad56aeb23b25c6572d97a23a6e84909adb24aa6ddb583be SHA1: 7faf57b24a140ba5d70d5959026d8d97f2c62c58 MD5sum: 386c37ae5522fdd57e7250e26d6b1f74 Description: simulator for spiking neural networks -- extensions Brian is a clock-driven simulator for spiking neural networks. . This package provides Python binary extensions. Package: python-cfflib Source: cfflib Version: 2.0.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 768 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-numpy, python-networkx (>= 1.4), python-nibabel (>= 1.1.0) Recommends: python-nose, python-sphinx, python-tables, python-h5py Provides: python2.6-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd10.10+1_all.deb Size: 217702 SHA256: e69867e547e17d131c2a7e0b5a7e3bb3bf12969249c33349bf35234c19863697 SHA1: 6d6d0e55f17beb741a83b2ce7b469789336bbe40 MD5sum: 51e4fd9582f9fb1106d4972f8d78285b Description: Multi-modal connectome and metadata management and integration The Connectome File Format Library (cfflib) is a Python module for multi-modal neuroimaging connectome data and metadata management and integration. . It enables single subject and multi-subject data integration for a variety of modalities, such as networks, surfaces, volumes, fiber tracks, timeseries, scripts, arbitrary data objects such as homogeneous arrays or CSV/JSON files. It relies on existing Python modules and the standard library for basic data I/O, and adds a layer of metadata annotation as tags or with structured properties to individual data objects. Package: python-dicom Source: pydicom Version: 0.9.4.1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1804 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.4.1-1~maverick.nd1_all.deb Size: 360124 SHA256: e11a5ca1d87ad166eefa550498743db961ae2727733b6f01d40d177c7d9c53dd SHA1: 3f767eb7480ef44c6c7a56a839f01e99f8cf849d MD5sum: 0adfc3a91a5b7419208ef94bb7a699a9 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.5.0-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2064 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.5.0-2~nd10.10+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.5.0-2~nd10.10+1_all.deb Size: 1456570 SHA256: 11fdf0c24ff28adfc2167a7aaf21d1d71277a004b35955be4137e76b0464d163 SHA1: 6f1fc9c239ea275f320ff2b07dd95b3140f8c182 MD5sum: 0479d8f35bb56a2f9fdd98557ccd9103 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.6 Package: python-dipy-doc Source: dipy Version: 0.5.0-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3220 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.5.0-2~nd10.10+1_all.deb Size: 1942710 SHA256: 5265ba81c45ef171a269d23c8c902c3d2b9e738cb16d49b9905f761a1700d594 SHA1: 3222ef2d67c8bf780f14d716bb2c36f9ebf0dbd0 MD5sum: ddf946187598988329e881c2a401795d Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-dipy-lib Source: dipy Version: 0.5.0-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 584 Depends: neurodebian-popularity-contest, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.4) Provides: python2.6-dipy-lib Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.5.0-2~nd10.10+1_amd64.deb Size: 221184 SHA256: 69f28e58ef647bd6f7b4c7a07ef57b88d4c70ee0fe091254c7293f6f92e02389 SHA1: c9bb308a0dd2a0a925e423a0630d9ec4e8a80932 MD5sum: a400d3a03d49255452da2f328a7fa66d Description: toolbox for analysis of MR diffusion imaging data -- extensions Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Python-Version: 2.6 Package: python-freenect Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 128 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreenect0.0, libusb-1.0-0 (>= 2:1.0.8) Suggests: python-numpy, python-matplotlib, python-opencv Homepage: http://openkinect.org/ Priority: extra Section: python Filename: pool/main/libf/libfreenect/python-freenect_0.0.1+20101211+2-2~nd10.10+1_amd64.deb Size: 33220 SHA256: c065b560327d7e07e2686109da6f8d415abdbf0350cf5e738f1d3d039260f0d3 SHA1: d6522a5cf2576f57c5ee5f920573f0f7f1243dbb MD5sum: 7e9869090cebd5551eba8f7b9b72eded Description: library for accessing Kinect USB camera -- Python bindings libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package provides freenect extension to use libfreenect functionality from Python and includes some demo scripts. Package: python-joblib Source: joblib Version: 0.5.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 232 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.5.1-1~nd10.10+1_all.deb Size: 44170 SHA256: f6ae1de5302149bb9f5fe544b8731a3ab5778c89a8084e0d82224e0babd24ef1 SHA1: 875386986148ee06c20ad5ee904321630aabefca MD5sum: 14f66b3c9a63845372db81a9cd8f19f6 Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. Package: python-libsvm Source: libsvm Version: 3.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd10.10+1), python, python-support (>= 0.90.0) Provides: python2.6-libsvm Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: python Filename: pool/main/libs/libsvm/python-libsvm_3.0-1~nd10.10+1_amd64.deb Size: 7610 SHA256: bd9663b6d36134dd9170ee1f6eee9c73170b51ea13ec3dbbfaee716c840533f6 SHA1: ab7dd46a0d835c0d7e9b35a26980c01a90d3c0e9 MD5sum: 616f9628b43baa9dbe32f893753e869b Description: Python interface for support vector machine library Python interface for the LIBSVM library using ctypes. This new python interface is provided since 2.91, and it is incompatible with the old one. Package: python-mdp Source: mdp Version: 3.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1812 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, shogun-python-modular, python-libsvm, python-scikits-learn, python-joblib Suggests: python-pp, python-py Enhances: python-mvpa Conflicts: python-libsvm (<< 3.0) Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.1-1~nd10.10+1_all.deb Size: 455316 SHA256: 8fff54b3773d8fa5d35682fee99430486bc02c91c7d67818527c5c52766e1901 SHA1: 9cc66ac92a72e1336e5592856507457e1f5aa21e MD5sum: 02763920802411c0a59966fcea7ea133 Description: Modular toolkit for Data Processing Python data processing framework. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. Package: python-mlpy Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 424 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mlpy-lib (>= 2.2.0~dfsg1-1~maverick.nd1) Suggests: python-mvpa Provides: python2.6-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy_2.2.0~dfsg1-1~maverick.nd1_all.deb Size: 55840 SHA256: 8283e4ed0f79671d42b030290729eda5c540214d36125e1b06fd0b5b3c9a21c7 SHA1: 4bd22103a057fe6eecf855acf12e7e077d38fc19 MD5sum: b0171fc77f895c31946c6d7aed80768d Description: high-performance Python package for predictive modeling mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, *RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators. Python-Version: 2.6 Package: python-mlpy-doc Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1132 Depends: libjs-jquery Suggests: python-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: doc Filename: pool/main/m/mlpy/python-mlpy-doc_2.2.0~dfsg1-1~maverick.nd1_all.deb Size: 478862 SHA256: d4a1ca68b49b3b1fa7a35abdc1e263f00970d4a6cfde9fd5388508b5c3273ce9 SHA1: 3a203bde333a49868ea27467bb04fa9eb6870af6 MD5sum: da3c2ef1ca5363c2774440218a32fb73 Description: documention and examples for mlpy mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . This package provides user documentation for mlpy in various formats (HTML, PDF). Package: python-mlpy-lib Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 312 Depends: libc6 (>= 2.3.4), libgsl0ldbl (>= 1.9), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-mlpy-lib Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy-lib_2.2.0~dfsg1-1~maverick.nd1_amd64.deb Size: 70820 SHA256: bbfb06fd94be6d0d3288014ace6eece19424cb7b7c2fe7743673d8c6f9d9a568 SHA1: 01d9f84c07de1f9b4e987bf20d41c9df399364e9 MD5sum: 25a7200ed3517d83a23bc8e928a374c1 Description: low-level implementations and bindings for mlpy This is an add-on package for the mlpy providing compiled core functionality. Python-Version: 2.6 Package: python-mpi4py Source: mpi4py Version: 1.2.2-1~pre1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1156 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libopenmpi1.3, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0) Recommends: openmpi-bin Suggests: python-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py_1.2.2-1~pre1~nd10.10+1_amd64.deb Size: 376202 SHA256: 8803845d8950c74d886f77f9c122a17ddb5ad2e90a62391ecaeb3370dec61ba6 SHA1: bbc3d3080caade391ab21f63d08414ddf34eb88a MD5sum: 8693100f20d29e0ee4ccb8417d76a087 Description: bindings of the Message Passing Interface (MPI) standard MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). Package: python-mpi4py-dbg Source: mpi4py Version: 1.2.2-1~pre1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2064 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.2.2-1~pre1~nd10.10+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py-dbg_1.2.2-1~pre1~nd10.10+1_amd64.deb Size: 563238 SHA256: 2a0f207f06dbfedc4aa1cde0ebbe36a6e7a0b6c1be01d69ce138b14e90266253 SHA1: ff8bf7e4cf55617a94bbfc8589fd5be250ee0f15 MD5sum: b43ff55aa584174ef00782c1c7e18bdb Description: bindings of the MPI standard -- debug symbols MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides debug symbols. Package: python-mpi4py-doc Source: mpi4py Version: 1.2.2-1~pre1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 272 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.2.2-1~pre1~nd10.10+1_all.deb Size: 54814 SHA256: 1bcb050af5a49af61314a1053fb07ff943c6267a9ca673e7bf3a391006844725 SHA1: 9aca99120fe1caf7b242bf68424c109eb12ba074 MD5sum: 6539799e7d5bce2eaf1ebfdd041f3e53 Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa Source: pymvpa Version: 0.4.7-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4064 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.7-2~nd10.10+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.7-2~nd10.10+1_all.deb Size: 2188516 SHA256: 3d3ba1b06e4dcd8238b878fcf4613d25dd267bffd2122668e7b5d4a4d1c96030 SHA1: d02fd18ddd9cd8dd72c1a3ced77ac550ae62e564 MD5sum: 0759d9dcaaaf7423a0eeda52f721b6b3 Description: multivariate pattern analysis with Python Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.7-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41144 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.7-2~nd10.10+1_all.deb Size: 8717354 SHA256: c771a0fcb1dbf2d51cd554d68e5b88a0c273115d84f9514365ea18548146c90e SHA1: 90d06dec36974c9891f508d7f3f81d847547c52d MD5sum: 19a615daa57fe6faae3744c4ce333125 Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa-lib Source: pymvpa Version: 0.4.7-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.7-2~nd10.10+1_amd64.deb Size: 35068 SHA256: 27b0c7506727929efa22e1a27503849b7a3606315f7d28fbd0452083a3fa2029 SHA1: fd0cb5b74b77c41946b3fded2cd023627d43cd5c MD5sum: fdc908488faa4260c948c700c3a63682 Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. Python-Version: 2.6 Package: python-mvpa-snapshot Source: pymvpa-snapshot Version: 0.6.0~rc4-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4596 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-numpy, python-mvpa-snapshot-lib (>= 0.6.0~rc4-1~nd10.10+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy2, python-mvpa-doc, python-scikits-learn Conflicts: python-mvpa Provides: python2.6-mvpa-snapshot Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot_0.6.0~rc4-1~nd10.10+1_all.deb Size: 2314464 SHA256: b76d18114b3a01cac4a108fa7c2135fa038c47dadf28d5372fded4d76b5959ed SHA1: 04ee707c2a94fb834c519f16ac4e6f7859898a18 MD5sum: 6c4c6f10add8b1f729a571176f4fa763 Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of a development snapshot. The latest released version is provided by the python-mvpa package. Python-Version: 2.6 Package: python-mvpa-snapshot-lib Source: pymvpa-snapshot Version: 0.6.0~rc4-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Conflicts: python-mvpa-lib Provides: python2.6-mvpa-snapshot-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot-lib_0.6.0~rc4-1~nd10.10+1_amd64.deb Size: 43562 SHA256: 497d11268dba715e57f4d403d637e99767f1d8703c1a3d5f1f134c7a7c72da39 SHA1: 3302b67b7e08c174abeb2148f07ca5629970f458 MD5sum: 6d78b72424643b24d40ef8265d377a3a Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. . This is a package of a development snapshot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.6 Package: python-networkx Version: 1.4-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2672 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-numpy, python-scipy, python-pygraphviz | python-pydot, python-pkg-resources, python-matplotlib, python-yaml Homepage: http://networkx.lanl.gov/ Priority: optional Section: python Filename: pool/main/p/python-networkx/python-networkx_1.4-2~nd10.10+1_all.deb Size: 647264 SHA256: b017027eef54f848795bccd39ea12a2dbea8a87dc1f41f67337820a83a670f1d SHA1: 0c489fa2a3363b77c331c3b0f70e9ab7b5dea954 MD5sum: 266fe36b0aff4b3fd67ab0a1d4022ea8 Description: tool to create, manipulate and study complex networks NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. Package: python-networkx-doc Source: python-networkx Version: 1.4-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15832 Depends: neurodebian-popularity-contest Homepage: http://networkx.lanl.gov/ Priority: optional Section: doc Filename: pool/main/p/python-networkx/python-networkx-doc_1.4-2~nd10.10+1_all.deb Size: 6213732 SHA256: 579c78d05cab19f1ccf3d85dd7a4e294a1590a130f3ff43b67488237504fd7c7 SHA1: a1eea5abeaaf193843999b87813fac5ebd1030e1 MD5sum: bd65e5e64a2d788e3947c5d2ff0afe31 Description: tool to create, manipulate and study complex networks - documentation NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. . This package contains documentation for NetworkX. Package: python-nibabel Source: nibabel Version: 1.1.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3604 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom Suggests: python-nibabel-doc Provides: python2.6-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.1.0-1~nd10.10+1_all.deb Size: 1665746 SHA256: 51cbae43af67247b4c78921c369abe1e3a611e81c46a0ddccc89da413346a249 SHA1: 168c12f80d00ef8d07f224d8b75c5f6b578dbf40 MD5sum: 57697de584a11b783e40a9f736adbce7 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.6 Package: python-nibabel-doc Source: nibabel Version: 1.1.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2748 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.1.0-1~nd10.10+1_all.deb Size: 405318 SHA256: 612d6bc5ef9527f0757d3e18cd4ee1b6c019adae09d9ce31b6bbce066753c6c9 SHA1: 2e81bd0dbc7eb867a8d094d63d6d41a537352af8 MD5sum: 89bc44603afece12d9598dfd6ad9f907 Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nifti Source: pynifti Version: 0.20100607.1-4~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1136 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libnifti2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, python-numpy, libjs-jquery Provides: python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-4~nd10.10+1_amd64.deb Size: 290910 SHA256: 944a92bf710005db9359f76ec97023b2b88a7372d5f37eac5410b18afdbdb9e5 SHA1: 28b12d5a1e128853b8a2d79bc92900c976ea5e41 MD5sum: 33d72dc11155c87e2c105074ea24abd4 Description: Python interface to the NIfTI I/O libraries Using PyNIfTI one can easily read and write NIfTI and ANALYZE images from within Python. The NiftiImage class provides Python-style access to the full header information. Image data is made available via NumPy arrays. Python-Version: 2.6 Package: python-nipy Source: nipy Version: 0.1.2+201100720-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3404 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-numpy (>= 1.2), python-nibabel, python-nipy-lib (>= 0.1.2+201100720-2~nd10.10+1) Recommends: python-matplotlib, mayavi2 Suggests: python-mvpa Provides: python2.6-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.1.2+201100720-2~nd10.10+1_all.deb Size: 702896 SHA256: 43935fe1db11a6a8727d0289f7c2408ad376e5622e8669f174336e5d68250af7 SHA1: f1e12e2c0a35904da3e20010522dcd08ecae0680 MD5sum: e289dbe8e38589c0d4d2a0af210d9005 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI). Python-Version: 2.6 Package: python-nipy-doc Source: nipy Version: 0.1.2+201100720-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9420 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.1.2+201100720-2~nd10.10+1_all.deb Size: 2513790 SHA256: fdb6880b3373c372a83061952068995e86b85f6211afe8a37496fa373c0b885c SHA1: 3f0251d4da2b6f123dc6668b57aa81827366ec33 MD5sum: 31bebc5a3fe9708a75b2eaa40b1f0741 Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipy-lib Source: nipy Version: 0.1.2+201100720-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2272 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.4), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0) Provides: python2.6-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.1.2+201100720-2~nd10.10+1_amd64.deb Size: 738824 SHA256: ab5d56e4c44e6fbff7abb8b89b2494bde19260d8a007579c8fff4383def263dc SHA1: f5b3361211fd669f6ed0385f952166ac5640ef0f MD5sum: 2b94cd11cf6847c7e638d6e0814f772d Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI). . This package provides architecture-dependent builds of the libraries. Python-Version: 2.6 Package: python-nipy-lib-dbg Source: nipy Version: 0.1.2+201100720-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2412 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.4), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-nipy-lib (= 0.1.2+201100720-2~nd10.10+1) Provides: python2.6-nipy-lib-dbg Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg_0.1.2+201100720-2~nd10.10+1_amd64.deb Size: 807886 SHA256: b80793ba388f7432ee4f07f33712c68931f2fb2b3c76be3acafc3147b40c0064 SHA1: 8d77b9ed77e9ad94362d635c87b51a06eb970718 MD5sum: f1698c7e2fcd2883b162fdd1a4b29530 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI). . This package provides debugging symbols for architecture-dependent builds of the libraries. Python-Version: 2.6 Package: python-nipype Source: nipype Version: 0.4.1-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2152 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.4.1-2~nd10.10+1_all.deb Size: 384640 SHA256: 9a76f5ef5674a0eba4c55dd5dada72e2e98b91c7d3d48593ea359cfc3161cfb5 SHA1: e24ede46d248042ec9e3c08c33c180cf543cdd8a MD5sum: 68fc6cbb3e7b0817071a8855fe8f54da Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.4.1-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4176 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.4.1-2~nd10.10+1_all.deb Size: 988240 SHA256: 8843ce816b59981cd24d049ff5ffaaee9d7658067e7743debb85d29851b37076 SHA1: bb224569d3c0fcd7d9e8bf3e1334ea97d99e1ddc MD5sum: f5e55393c2e9272bc51b08bead3b22a5 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.3.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9404 Depends: neurodebian-popularity-contest, python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.3.1-1~nd10.10+1_all.deb Size: 3902354 SHA256: 9116c5ff20793287338e375bb155f4b5f16c461bee3944088d6be57593cd6520 SHA1: 40a47a38b56142cdca14640f1f11c99b3f495434 MD5sum: 254a9d41c221d536a8d2397a9b1b2da9 Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.3.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6940 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.3.1-1~nd10.10+1_all.deb Size: 5194126 SHA256: 1aa39f1d1f9ec34635e5d52ffe18984c36cc3a1f0d4e2e154b4abd17080c750e SHA1: 2119549645db444566c6e19a77d776f28ea6ecb4 MD5sum: eaf3a990538dff63228e6745b2d178cd Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openmeeg Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-openmeeg Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: python Filename: pool/main/o/openmeeg/python-openmeeg_2.0.0.dfsg-2~maverick.nd1_amd64.deb Size: 157526 SHA256: 3a95190b1f9aa9128f1ff07a9927bf4285f605c029ff87b7f763905047efe436 SHA1: 303aa983399d068350fe8c0b289533ecfd0ee69e MD5sum: 59f8f5cd00801d0d43f458256051b35c Description: Python bindings for openmeeg library OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides Python bindings for OpenMEEG library. Python-Version: 2.6 Package: python-openopt Source: openopt Version: 0.34+svn1146-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1448 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.34+svn1146-1~nd10.10+1_all.deb Size: 206378 SHA256: b106a0d66a2ef97bbca4d2fb148bf1e94a469170720ce8184dbad0db358e5d65 SHA1: 062ae87a5cc5a1f04475cd716ec8001c6d82627b MD5sum: a2b14bca5cd762d80354bf71ff382a66 Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6 Package: python-openpyxl Source: openpyxl Version: 1.5.4-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 440 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.5.4-1~nd10.10+1_all.deb Size: 60862 SHA256: d61dd26b473f399cd4458dc2a1916d9401857b0f864e9f694c2d29468861aa67 SHA1: f60187f8dd0d9cee1a9e4947c2c0f7cec0b7a364 MD5sum: 7583fe3b7a2d03ea7d899935aee7b76d Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pyepl Source: pyepl Version: 1.1.0-3~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1696 Depends: python (<< 2.7), python (>= 2.6), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~maverick.nd1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>> 1.0.22), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0, libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.6-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0-3~maverick.nd1_amd64.deb Size: 381566 SHA256: 5c2db2b03d580c1c40aeb4768b772aae61ebab32a1564b46d58eab61efb2e4a1 SHA1: f900b6fb4e1cabbc59cf0d16db41753165d0422c MD5sum: 8f8968da5e76f1f8748211134d2e645a Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides PyEPL for supported versions of Python. Python-Version: 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 852 Depends: python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0-3~maverick.nd1_all.deb Size: 817816 SHA256: c48e4f818f9d5f08f12d59d6c1131b7f163f29c8801d3373889a7c20795d2d9f SHA1: 82228fe2202df811391f45df27ceaabcc7abca89 MD5sum: f4abbefb9d51c38b9370f48229577970 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pyglet Source: pyglet Version: 1.1.4.dfsg-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4356 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-ctypes | python (>= 2.5), libgtk2.0-0, libgl1 | libgl1-mesa-swx11, libglu1 | libglu1-mesa Recommends: libasound2 | libopenal1 Provides: python2.6-pyglet Homepage: http://www.pyglet.org Priority: optional Section: python Filename: pool/main/p/pyglet/python-pyglet_1.1.4.dfsg-1~nd10.10+1_all.deb Size: 972184 SHA256: 48cd04fec2110ce036d16fc32550296c817eef64f138a48b54456bb177ade3d0 SHA1: fe964cc057d4573c9e10663f8a9e2c78b4b69a80 MD5sum: 95d34f27e08151c595ef5e023ab56fe9 Description: cross-platform windowing and multimedia library This library provides an object-oriented programming interface for developing games and other visually-rich applications with Python. pyglet has virtually no external dependencies. For most applications and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. It also handles multiple windows and fully aware of multi-monitor setups. . pyglet might be seen as an alternative to PyGame. Package: python-pynn Source: pynn Version: 0.7.0-1~pre1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1004 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.0-1~pre1~nd10.10+1_all.deb Size: 171194 SHA256: 6baf0a7df0477e569f6a4335a861ed6b4c2fcf5aa990b8f8ba3eba2183911d67 SHA1: 6ea818377c0d35c2f5612fa79b98f818b83c5c9e MD5sum: 593f269af43bae4f52e44df45db94407 Description: simulator-independent specification of neuronal network models PyNN allows to code for a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-pyxid Source: pyxid Version: 1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Homepage: https://github.com/cedrus-opensource/pyxid Priority: optional Section: python Filename: pool/main/p/pyxid/python-pyxid_1.0-1~nd+1_all.deb Size: 11020 SHA256: 1031c0d69dd73cb38f3e0b826193211706a94bfd04da4287288418b257e54249 SHA1: 0f0d0524354e5d07eb89efcb11779d9acd9d57e2 MD5sum: 1f2a9bc07952b1f5c6b65fc5c092f75c Description: interface for Cedrus XID and StimTracker devices pyxid is a Python library for interfacing with Cedrus XID (eXperiment Interface Device) and StimTracker devices. XID devices are used in software such as SuperLab, Presentation, and ePrime for receiving input as part of stimulus/response testing experiments. . pyxid handles all of the low level device handling for XID devices in Python projects. Package: python-scikits-learn Source: scikit-learn Version: 0.8.1.dfsg-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1424 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-scikits-learn-lib (>= 0.8.1.dfsg-1~nd10.10+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-scikits-learn-doc Provides: python2.6-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn_0.8.1.dfsg-1~nd10.10+1_all.deb Size: 311482 SHA256: 305ac8ee4426f2847d3d305af98d84313dcb18c0b4fe08a28d29242eb3e999a8 SHA1: f40482b8fdbca3179b1e5bcbe29740b7a40fcf89 MD5sum: cdcc780e2db9e48c5d4d3cb9043da917 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.6 Package: python-scikits-learn-doc Source: scikit-learn Version: 0.8.1.dfsg-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14620 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-scikits-learn-doc_0.8.1.dfsg-1~nd10.10+1_all.deb Size: 9030822 SHA256: 087ebd24d9248eded35cb543663dafde620a7be8f6a5b619b57e226918e73e33 SHA1: 4ce97d0415afc5e5dbd807482714e43aec46860c MD5sum: addd578287615a90a4b80026ff9b1ddc Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-scikits-learn. Package: python-scikits-learn-lib Source: scikit-learn Version: 0.8.1.dfsg-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1380 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-scikits-learn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn-lib_0.8.1.dfsg-1~nd10.10+1_amd64.deb Size: 524020 SHA256: e919463f1d72dec54e71885ac06a2dc46366c0eaa8a2cbd1c15c48628025e0bc SHA1: fe6667b3ebc6c6db424309fd0383b3e86d9fbf02 MD5sum: 548ece4b3e0bf9379cf99ba2b312c1c4 Description: low-level implementations and bindings for scikits-learn This is an add-on package for python-scikits-learn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.6 Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.6-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd10.10+1_all.deb Size: 9804 SHA256: 4fcd21c344b9f0a85984ff9d8b72caaa98c09051d7fae62f1cf4d73c083fbb5d SHA1: 3a9e2d644523a5be0465ede68d708dd7d27c2d9f MD5sum: 79297e6e7d5e162b1a9bc6dce8cbe820 Description: Simple generic functions for Python The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4188 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-docutils (>= 0.5), python-pygments (>= 0.8), python-jinja2 (>= 2.2), libjs-jquery Recommends: python (>= 2.6) | python-simplejson, python-imaging Suggests: jsmath Homepage: http://sphinx.pocoo.org/ Priority: optional Section: python Filename: pool/main/s/sphinx/python-sphinx_1.0.7-2~nd10.10+1_all.deb Size: 1260220 SHA256: f28f090879ac7000b7776776b7abf460bd1fa769febf27683218710014d1ea0f SHA1: 617ccda8c98c0be4c7d6ab50a61699a10c19d219 MD5sum: 6927c8799a2a3a86523898580e080d10 Description: tool for producing documentation for Python projects Sphinx is a tool for producing documentation for Python projects, using reStructuredText as markup language. . Sphinx features: * HTML, CHM, LaTeX output, * Cross-referencing source code, * Automatic indices, * Code highlighting, using Pygments, * Extensibility. Existing extensions: - automatic testing of code snippets, - including doctrings from Python modules. Package: python-stfio Source: stimfit Version: 0.10.18-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 456 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python-numpy Recommends: python-matplotlib, python-scipy Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.10.18-1~nd10.10+1_amd64.deb Size: 193578 SHA256: 3f7fd13526f5c68a644ff5016e23b80f18331daa886e6bca7274697c17de9330 SHA1: 8d7932c272d6b97c2f5601ea82282142932fd29a MD5sum: 5e5ecb93868955248ab22332947577f1 Description: A Python module to read common electrophysiology file formats. The stfio module allows you to read common electrophysiology file formats from Python. Axon binaries (abf), Axon text (atf), HEKA (dat), CFS (dat/cfs), Axograph (axgd/axgx) are currently supported. Package: python-surfer Source: pysurfer Version: 0.1+git21-g55debc4-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.1+git21-g55debc4-1~nd10.10+1_all.deb Size: 21890 SHA256: 0f4d805a7b89fccbd560ba8fffd1a906206705136709690e8011696048d0ab9e SHA1: fab70e7e426b812d4e46ec44db4f994bb524a592 MD5sum: 6c212f6a31b50201f5ff56acd13b2011 Description: visualize Freesurfer's data in Python This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. . PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets. Python-Version: 2.6 Package: python-sympy Source: sympy Version: 0.6.7-1.1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9268 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Recommends: python-imaging, python-ctypes, ipython Homepage: http://code.google.com/p/sympy/ Priority: optional Section: python Filename: pool/main/s/sympy/python-sympy_0.6.7-1.1~nd10.10+1_all.deb Size: 1696290 SHA256: 93ac728b1ec8442b324d0a353c6fc7606cc1b2623934399301b8e1626c5497c1 SHA1: 561bfc91c596b02c7212a563936557dea2e4fb1b MD5sum: 675f1e681ed76bd6478e9e9f2d7b3a24 Description: Computer Algebra System (CAS) in Python SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. Package: python-traits4 Source: python-traits Version: 4.0.0-1~cbp1~nd10.10+1 Architecture: amd64 Bugs: mailto:bugs@neuro.debian.net Maintainer: NeuroDebian Team Installed-Size: 2124 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0) Suggests: python-traitsui Conflicts: python-traits (>= 4.0~) Homepage: http://pypi.python.org/pypi/traits Priority: optional Section: python Filename: pool/main/p/python-traits/python-traits4_4.0.0-1~cbp1~nd10.10+1_amd64.deb Size: 342140 SHA256: 0c61d859dc88d649c85ac0e46cb0bcfb1e30e14a63229bdfd73aea39a5b399d1 SHA1: 265632f7a697d6e0cf12ff9cb05eb52b8f5b8dab MD5sum: 94d9361438ad8791fcf9c6baaa5fe5ca Description: Manifest typing and reactive programming for Python The traits package provides a metaclass with special attributes that are called traits. A trait is a type definition that can be used for normal Python object attributes, giving the attributes some additional characteristics: * Initialization: A trait attribute can have a default value * Validation: A trait attribute is manifestly typed. * Delegation: The value of a trait attribute can be contained in another object * Notification: Setting the value of a trait attribute can fired callbacks * Visualization: With the TraitsUI package, GUIs can be generated automatically from traited objects. Uploaders: Yaroslav Halchenko , Michael Hanke Vcs-Browser: http://git.debian.org/?p=pkg-exppsy/python-traits4.git Vcs-Git: git://git.debian.org/git/pkg-exppsy/python-traits4.git Package: python-tz Version: 2011h-0.1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 176 Depends: neurodebian-popularity-contest, tzdata, python, python-central (>= 0.6.11) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2011h-0.1~nd10.10+1_all.deb Size: 45714 SHA256: c25c43eed03bc16a4a4b76ac2da61dfa2e16ae84b8c70bbab9b631f3227bceb1 SHA1: 3f76e405d2c50f1dab8a1738b67feed318184a46 MD5sum: 92b95bf908dd71bcd0b76de98454b96b Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Python-Version: all Package: sigviewer Version: 0.5.1+svn556-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1044 Depends: neurodebian-popularity-contest, libbiosig0, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.5.1+svn556-1~nd10.10+1_amd64.deb Size: 440812 SHA256: aa324a81669408f28c0123466a67dbe28d9d9d8024d80b1da6e062d3577788bf SHA1: 9b35ab703f15f3148a8d33a3f6f8ec50ed611b36 MD5sum: 841ccf9e595445f38d5728de62b6a1af Description: GUI viewer for biosignals such as EEG, EMG, and ECG SigViewer is a viewing and scoring software for biomedical signal data. It relies on biosig4c++ library which supports a number of data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . Besides displaying biosignals, SigViewer supports creating annotations to select artifacts or specific events. Package: spm8-common Source: spm8 Version: 8.4290~dfsg.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22192 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4290~dfsg.1-1~nd10.10+1_all.deb Size: 10547304 SHA256: 7535453b5cb043adb69c504dd8278da9ed3cd138e1dc63de1445010e73b73e1d SHA1: 5d9ac3173f34e823da382333ec734fe809a075d5 MD5sum: 6c707522636ec13a8211bdd61bdf647c Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.4290~dfsg.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73084 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4290~dfsg.1-1~nd10.10+1_all.deb Size: 52167536 SHA256: 21c5936452e8e395dce1eb87a27e6d5e44ecf31c3e115c33cabc16d481dd5b4a SHA1: 8cc553875b00c2fda02201e56e40e4f704a0c912 MD5sum: 617a4cc7e6c2d742ad07657521653fb2 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.4290~dfsg.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9380 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4290~dfsg.1-1~nd10.10+1_all.deb Size: 8648806 SHA256: 69816479e39f295424837f9f365776230c896127cd8b8039137788276f0d9783 SHA1: e8526aa7a841fd6191aabda15fd93907b303cf72 MD5sum: 14db5a1cce0c78dbda4fb664196514d7 Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: stabilitycalc Version: 0.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd10.10+1_all.deb Size: 28610 SHA256: 42181fb7c6894e705ff4d22e2642b5ba9c6be9441cfdcc18a1a091c31c315ebf SHA1: b268e8436d9662df1de68db4d2a8f27cf1eef5ca MD5sum: 2db6de1eeef334b324f51cbc12bcddaa Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.6 Package: stimfit Version: 0.10.18-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2084 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.11.0), libwxgtk2.8-0 (>= 2.8.11.0), python-wxgtk2.8 (>= 2.8.9), python-numpy, python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.10.18-1~nd10.10+1_amd64.deb Size: 737228 SHA256: e1d6b88372c83c06b821b9ddcbef6386427f0703682782bb69a01bd49173d1f5 SHA1: 22a0da07adb2a59a4f936ddb0a3d3b7ae2c12be6 MD5sum: ef9820dade287f31cb8722af8265dd06 Description: A program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.10.18-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15264 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.10.18-1~nd10.10+1_amd64.deb Size: 5137010 SHA256: b18ddab343f43bae98073f9ffbdd5e26c6bccc954cdf3575f1e2f4aea66b2054 SHA1: 958f12d90423cd1be63c61664bb11c5551aac8e7 MD5sum: db946adfb727c1ae624281336e97a547 Description: Debug symbols for stimfit Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: svgtune Version: 0.1.0-2 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 64 Depends: python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-2_all.deb Size: 6680 SHA256: 69b4df1e0b4c247673265c7f5bb2b2ffe2209d783617bf7f6eadce86633f80e1 SHA1: bec339e4453c35a05a616deef6769a6f2ad2d00d MD5sum: ef6797498477a73f930ad9bc0db3ba73 Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png). Package: voxbo Version: 1.8.5~svn1246-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10132 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4:4.5.3), libqt4-qt3support (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0), libx11-6, libxext6, libxi6, libxrender1, zlib1g (>= 1:1.1.4) Suggests: mni-colin27-nifti, matlab-spm8 Homepage: http://www.voxbo.org Priority: extra Section: science Filename: pool/main/v/voxbo/voxbo_1.8.5~svn1246-1~nd10.10+1_amd64.deb Size: 3708612 SHA256: dec56bf3009e7939a9c1bdfef2e49a7ba2899718a6bc79de63d8989c030aa1dd SHA1: e3e555a3f2b487d958e8bf16c1d63a35b43ebfc8 MD5sum: d0025377c53ae195f0514a76f388b2d2 Description: processing, statistical analysis, and display of brain imaging data This is a toolkit for analysis of functional neuroimaging (chiefly fMRI) experiments and voxel-based lesion-behavior mapping. VoxBo supports the modified GLM (for autocorrelated data), as well as the standard GLM for non-autocorrelated data. The toolkit is designed to be interoperable with AFNI, FSL, SPM and others.