Package: ants Version: 1.9.2+svn680.dfsg-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 35356 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_i386.deb Size: 10985642 SHA256: 2f2e8d7ae90f326481fc8d0d157a55a29f2da19405ee1a4c754d72f7f99abd98 SHA1: 2259a9d20148d51d82eda6099128f95d82038987 MD5sum: b97d06179a6e7039f57c16f0d1f21f87 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 44 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_i386.deb Size: 13516 SHA256: 851ed4f6a263b17a3ceafe945cc7bf74f41f37052ef095b5b3b52707bd77f4ce SHA1: 0f183c1fe5e147cdefc9a292f62f5a2196424a18 MD5sum: bb39d70a8f7280a2d6bc7b3e9ed75a8a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 19312 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_i386.deb Size: 7502570 SHA256: b84a027ecfff01568a2e8232026cfc2da26dacd10aec620059b01a26d2c33e3f SHA1: bf4977d8b1f5bee72bd530331d8773632b67ab16 MD5sum: cc8570678a1f99f85202bc63ca2e71d0 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: classads Version: 1.0.10-2~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 80 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_i386.deb Size: 30450 SHA256: 9736e08b182cf5969c2198168f001830e1a40b991acee050e8f86c5ab857771c SHA1: 56b7936ad2a60068805c6a7175cc560a209d214a MD5sum: 109eb61b6e67b10eb3a40858785c07d3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 10944 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_i386.deb Size: 4185484 SHA256: c94f7614214cb73988ef5397e0464e187241697f1d53efb79b67983f787b7f5a SHA1: 72a1ad3fe14884e1f2e2be1178727ebc588fd55c MD5sum: ca3af474eba14c3d7712aa1f642944bb 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 31736 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_i386.deb Size: 12508400 SHA256: 5498344b49e39e65c098a2347380619689995abff939007beb10a64e71749431 SHA1: c8c5e8a286758f0287dfc3d3b86e9b99fae76255 MD5sum: afddc1ae639974cfc94aa2f4e1bad738 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3572 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_i386.deb Size: 1355418 SHA256: baddebc9925f852ca286a8c078475256d26c4be86ffded3a2083fd64699092e3 SHA1: 9b4817d9a56ca2e8d6d3340b42550400decab921 MD5sum: 8bb4041d9d9d4bb7b853f529dbd01a82 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1024 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_i386.deb Size: 235784 SHA256: 4cbb796301c61e6138ec7e5583bfb1c03ebb99ae67f17931d3ef8c7c1db205c0 SHA1: 680d88b54cc75797c4dfa60903fdd05095c1372b MD5sum: 18dc8e89769fd08035447e435d9bee02 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3508 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3.6-6~) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd10.10+1_i386.deb Size: 758174 SHA256: 0a4cb1be3390b6332fa7de4b6c1c984fdd35e5f497482d95aecf1e95d6905e69 SHA1: 01e95a582861a39eb767e24557d0f11e33a4fdca MD5sum: e76022cb6f8d25c29c869fab18fedc4e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3868 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.3.6-6~), 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_i386.deb Size: 1459994 SHA256: 4198c478a47494fd518453571479ce59f1263453507bceb4681e6e41f84bd32e SHA1: 477dabb507e76ba854bae394815d67e909a78dc2 MD5sum: c1eedd830558e8a8a55996ef70bf0982 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 68 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_i386.deb Size: 33480 SHA256: 8868dfb9c1730fa2f966fa5ff419f1fc63918cd6f377afc80763985ed0ca1c36 SHA1: bf6da3bc8d14b4fcf9df6821a02f096f1606e89c MD5sum: 6ee572cd600eb7ea7486af9a60405173 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 436 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_i386.deb Size: 147076 SHA256: 91df5c4a4c74b52cdb338bfdf0e6470b534a1b7a2c5d3c5c8e51f143b8d94d8a SHA1: d29fd461affc45caf4e39a0f6be48f0b50d6f45d MD5sum: ff4222c40bde39ef13f3a5cb1e471aad 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3720 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_i386.deb Size: 1594156 SHA256: d45e326cea8b6625eeffc3b1fdb19aa33403af714ccf73f08308692f3ead0823 SHA1: 57286796d5742c18d2ebafd54980b818901823b2 MD5sum: f40d7d2fd356f7919c830c6510b08724 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 17576 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_i386.deb Size: 6171890 SHA256: 724de944d0cb2ee914c232a583ded5f35e4897e60adfe3852768c7c7518f17c0 SHA1: 253c2d1fdad914726d97b5a28837d76407f27538 MD5sum: daea103755409618db9bf9438ab41119 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3) Provides: libeatmydata Homepage: https://launchpad.net/libeatmydata Priority: optional Section: utils Filename: pool/main/libe/libeatmydata/eatmydata_26-2~nd10.10+1_i386.deb Size: 7940 SHA256: 4fab4998f71df4407020c5da2884ac86f40db12861b02808c6deaf8287a6b82d SHA1: 8519b346961d5b64516e10a4f3d69bbe86804365 MD5sum: 9690d2b6e3d1197256a4edda2a8a1919 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: i386 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_i386.deb Size: 2538 SHA256: 5c6d84c9439988ae8abdd62d13ba120780d8198907cc90763657bf6fd8b15d4d SHA1: c24e0beff126da18cb8c08deb6567d46b5d2e61e MD5sum: b22794a392896d1f452d143e2a153526 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: i386 Maintainer: NeuroDebian Team Installed-Size: 3832 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_i386.deb Size: 1477874 SHA256: ce34a281932921cb1e44edbaddb1cfcf6f8863d84f61a1e12b7669b6759baca6 SHA1: 4ce5b7952116fda9e95239f5383960d41ed530ba MD5sum: fd40d6168f4cf389b78f47d60736e7cd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 108 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_i386.deb Size: 38224 SHA256: 2b7d58ed3c49f4af703ce357d53a0dc7e36f5f9b09a0d83726791b6512805af3 SHA1: 2706ee5d495ef0e0edb324ead8dde28b5c8f92ad MD5sum: 9502189ddc6db889a304c8b411b4ac9c 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: i386 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_i386.deb Size: 28706 SHA256: ad377655a84bb88f5cc20c052cb55d7a7d274b4e88cdfccd76963fd084054207 SHA1: d8467b3f02d7abcea154d08cec72d8a8ea386569 MD5sum: 3762794dc8512d528b5f035b0d57eba2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 324 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_i386.deb Size: 111768 SHA256: 5209e7b1324cc93dce3d05b898acbfaa41b1f13a838a50d6b6fd5a0042da1b90 SHA1: 8b15f656b2334c0d2e8a281e38ff1ced8c5782d0 MD5sum: 3f6c8ed092a29350b7a906ea7aec2e26 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 8132 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_i386.deb Size: 3619290 SHA256: bac6d44487559b4499ba0dca455f2cb76534f0c9de24a5405bb72c6a9add852e SHA1: 871af06bb7512bd37a212a69c8a6ef7a58a0595c MD5sum: 564987cb40bce88bc7ee413186a88605 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: i386 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_i386.deb Size: 20656 SHA256: db40a75c5aaf03cbc58e84019be5c4d87bb8591359dadaadf4e02c6b3c5b00b2 SHA1: d71cabba99de86590412fe4e40e3aa26e17ec28e MD5sum: 9291c6f34affe351b1bcec832d82fb1c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1224 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_i386.deb Size: 376356 SHA256: 40e12b6df4284acde770f216c75fc4ed26c130205a4bda3e840b02e898e6b934 SHA1: f39c14da80c1a320771b9f40bebd47738b683637 MD5sum: 5177062449d2cd68f575c194d4110df3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 756 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_i386.deb Size: 294418 SHA256: 0348b07b25721c0a9f71565d2849bde5a0a77910cfafa74024a1fac88b119f81 SHA1: 6adfe69043d520cc633087880b6166204056ddcc MD5sum: 2ce7db88cefd9bcde2482fceaa1fd153 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 188 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_i386.deb Size: 57360 SHA256: 2f6e1513601b0ce7cb2ca0da2d14926ff80ba0a9153171291b374d8965085f95 SHA1: 78b5cc40baae638f1a28e3619eb40d183d57fd96 MD5sum: 712938ec48f0232575f80c5d7c7904e2 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: libclassad-dev Source: classads Version: 1.0.10-2~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1656 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_i386.deb Size: 525718 SHA256: 19f4c6628357b97e7febd7561e3bddc0a2a7d7cfa8d33e94dcc61f1650af190c SHA1: 8af37963ff3bea1692516382cf236fb5ac842f6a MD5sum: c57b9938c46e45cb7967da13daa69dfa 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1040 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_i386.deb Size: 416144 SHA256: 47af050f3219bf92849e7621f517ff2ecce1c7de1d12e6c00b056fe6ee4a2d1c SHA1: 272a333377819c8c653665e42a67fe7bfed42947 MD5sum: 294beac7f5f0b886fde05267deddbc08 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1004 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_i386.deb Size: 416200 SHA256: b9402cafe3b0101eac36ea5ba0840ca4ae9de5b126f4d8b43f5f450d54604537 SHA1: 725563a8cd2b5319e61d64f63bec01590aa71849 MD5sum: 4f40f40178bc5cb05a8a6a7092923adc 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: i386 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_i386.deb Size: 6626 SHA256: ec21a3db98ebbc14ac96a9fa731f37224faf59d8fc16c945640d48163cdcbf8e SHA1: 4cfb0c3148b56ec0bc38426297bb27d245dfba8a MD5sum: 48664c2404bdc22fd3a584604f70686e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, dmtcp, libc6 (>= 2.1.3) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libdmtcpaware1_1.2.1-2~nd10.10+1_i386.deb Size: 6272 SHA256: 4b36a15a583bd107add3cf7ca6a965eec57fdbbe9a486a959dcee07a9a4934e4 SHA1: d4dfd3dd38c8e894d42de5ba7d1fe44ddadfab59 MD5sum: 7f8ad54a1e02c2322ad135df18b75651 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: i386 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_i386.deb Size: 509884 SHA256: 2b3434919a1b55ff630454d308e0cd75bc0eb425ee8ed43f7a10ba9c86d68032 SHA1: 2dc2a66b722e677b577b0b17d75a1b4a380c8e21 MD5sum: 30454481876d22f5d7f99834c7a3c8a6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 23596 SHA256: f705bd3f6db7ff5f70ea99b3c583a1619d94abf631d0a7783ccf656294263a13 SHA1: 5bdb397c8445a30594af5bc36460d4ff57b1523c MD5sum: 6fc1c85e00c1c41a0b69b2fec84a8282 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 76 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_i386.deb Size: 18018 SHA256: 9e8861aac17ca8d272bf788540bb1c2978fc8369dede458d2c5f26d97ca0c54c SHA1: 369ecc59529069dfc46c00bb78113316bf8d71c5 MD5sum: 5f560533b777236b3395b63adb347be6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 88 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_i386.deb Size: 25978 SHA256: db9de1420dfb974a8770c690b02619162bafc7fe3868b0e28b2759371a77a576 SHA1: e494e5b3f16b03bf853b54aba39331455354bcfc MD5sum: e8d23ec6bccd6dbb9ac2e5b4062c0da0 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: i386 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_i386.deb Size: 18534 SHA256: 368808173f7428d12198fd50f624d801aef290c8419c59849fb6b210f910a230 SHA1: 80e3ecf7fcce61e6c036714df2cc9bc95afae170 MD5sum: d941f1ee5232ce9c359f617ce6530e72 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 296 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_i386.deb Size: 102756 SHA256: 0181addcbe743ccdcbd91edfb4b80c847429ff227a7df608ad5cb8b0c46cb990 SHA1: f8cc26b103450fe24683d97190e4aed06591affc MD5sum: acefe0a1fb5a1e8fe7bc421491d98585 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3588 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_i386.deb Size: 1089548 SHA256: 27c5cba6c43d39f07c6f9fa8a1f50dbb50aa06b0a04b921877c4cd1bb5b6e673 SHA1: 5b3f1cb420bcc912efc5785b6db8238b052b9e80 MD5sum: b27b8e1209a48db7308d1be691269abb 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: i386 Maintainer: NeuroDebian Team Installed-Size: 208 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_i386.deb Size: 62362 SHA256: e7645b4762143dc3e4a6c6ecb6c88e72dc822a57921f504fb1e2ca8c9fb44698 SHA1: 6e3a1cab0bf1605f1d09636a81882c4908f9ce7e MD5sum: 5ce79f3b3df0df7d1d29590785b1a9da 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: i386 Maintainer: NeuroDebian Team Installed-Size: 176 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_i386.deb Size: 57236 SHA256: 44cf7e5acd2cb6d43f5bffbfc5e1110b5868b1a8e0ea110eed8b968574af5d49 SHA1: 34fc692fa9d906630488a5a3b8def1b36d5ba8c2 MD5sum: f14e2a0f09e5fba43c32c81d4596fb73 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 356 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 110736 SHA256: 468cae1bb6f2ad9403c4405d37e6a7adfc542a5eb0ae52285a2c77c4a0797f82 SHA1: 489fd35f778595a684b17df83346d843c55d964e MD5sum: 0cf27da28c3ca99d1d140e1fc9187af6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1332 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_i386.deb Size: 235480 SHA256: 61243307684de333e8037585e744cc63e7a00dec4235d85218492ed3b31a2578 SHA1: 0f5be96d0b30d87e5cdc1d6ebca1485b6c5dcc4c MD5sum: dababc1064fc4d970e1e54576d472884 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 324 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 99468 SHA256: b265ffb44624c1289c5e91ab404bbec3f7bf9d886305f3da5c961c03db74d577 SHA1: 2ca09c70a5bedbe17c882846a45b23cc13a98647 MD5sum: ad42ef09ad2b4fb727d464cd01da8591 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 376 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_i386.deb Size: 93348 SHA256: dbad926e969ccd52553262a46c0be0803e2912d26a65cda52ae076c5d6b810f4 SHA1: 83d7a773b55156cc406c1b1011ed79e3f5f8aba2 MD5sum: 8692427ab6b84d05b84ed99e7b1a51f1 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: i386 Maintainer: NeuroDebian Team Installed-Size: 460 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_i386.deb Size: 151424 SHA256: 02dd63229792f2671cff56a1d0e0f670961084cd3ec39eeeaa0e438398ce3096 SHA1: b09d8a64ea93c72bd083452d2af14a696aeee2c1 MD5sum: 0e4089404ec474109398e654a5118c53 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: i386 Maintainer: NeuroDebian Team Installed-Size: 304 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_i386.deb Size: 107384 SHA256: 150bca33f426cc89796db8958d1b03730f80e4ae1d347039ba70ebfe2eabcd82 SHA1: a3a6f9736f7a0e218254e5cac55a3f2be26cdcf6 MD5sum: cea5d13d0539b1fdb36067594a636348 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: i386 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_i386.deb Size: 43856 SHA256: c92c21a2772e96d232289dadea0671887abf2b868ae988f209cd5b6c9eb574f1 SHA1: 4c7d482ca1198fd762853f13a387d0abd6a10bcd MD5sum: f51b11e44be747a9ba4f7c8d048907d2 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: i386 Maintainer: NeuroDebian Team Installed-Size: 816 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_i386.deb Size: 233088 SHA256: aaa12f77bef369fae972b97534252d484e564f16a03d1834e28c9da7c81249d1 SHA1: 31d24420ada6c4e1ab753fb7ceef7e2dede0f0fb MD5sum: 395f4db513ea6d5f2eafc4b001ed1920 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4896 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), libopenthreads13 (>= 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_i386.deb Size: 1576638 SHA256: 0414317a9be2cd8afa741cb203dc3f4e2392a59c462fd938d787612d9c1663fa SHA1: 2de41f43346b0362f7447b2385cd8b8bd16192d9 MD5sum: 4171d78c9105f082cf5741bdeaa7c1d7 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: i386 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_i386.deb Size: 262304 SHA256: d71fbd8566c9437846b68b6ebf6e2560bcbffcee637ce6481a8ac26f2341cc5d SHA1: 8e687004215f33db00254450b65f94b5e4f8c7b7 MD5sum: d1b5b3beae342cc4e48975e7027b7174 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: libsvm-dev Source: libsvm Version: 3.0-1~nd10.10+1 Architecture: i386 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_i386.deb Size: 28196 SHA256: 8a85a931de6432244d3273c6aadeb2fa8c619b9d042c8ee2fd4a6103b511da20 SHA1: 7a3698e7b7a261164b096cc78966a1639da70639 MD5sum: 3e45807e16947d3c4e4dd34f2e0d7c07 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 324 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_i386.deb Size: 111950 SHA256: b60a8caca2df20cbb19a877c83b8ac9b027c15a72cd8fb34aaa0579615dc9317 SHA1: 78bcaf7191e74de731d4bf36bd5fe3fa5eb96229 MD5sum: f321a4c4c944d4fc57ec61ace4bbb974 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 120 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_i386.deb Size: 42606 SHA256: 05aa647503b150ec20d28ef08f87d01ead0e2d2ba338675e39ce86f37f31325c SHA1: 9e86967668b8e19f1c6cb55d783175de0c2a03f0 MD5sum: e7b14153243546501c9e59fd6fb3e2a5 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: i386 Maintainer: Michael Hanke Installed-Size: 3616 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_i386.deb Size: 1271960 SHA256: a05ad0462cbf1c0dcdc33fee836640ec263e390a3c25ee24e19381606e6f5b02 SHA1: 26976df4dd314ad7144d4d1922ceb49066dafde9 MD5sum: acab84d96e8d07cad4dbfc55f5c89c7b 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: 2.0.203-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2100 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_2.0.203-1~nd10.10+1_i386.deb Size: 758110 SHA256: a5aca1ee7c78c09fd069d2f54ccb975c08792d3b2e3e4a3be6ac126cc0ce33f4 SHA1: 00ed2f93b55dbc7a73219f4129a3cd47ffbf41e5 MD5sum: 81392b2524872dd8b258c7c58497ba21 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 10712 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.29.3), libc6 (>= 2.3.6-6~), 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_i386.deb Size: 4075146 SHA256: 3345c1b9cfa17a0a836068cc74eafc7f3e54825e84f5de328a9a6a5d828506be SHA1: 0344a0720998e0803b9feca8ddfcd85768a2e6c6 MD5sum: 5f17b94e945991e5ec3e37f9c3adfbb1 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6384 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_i386.deb Size: 2218776 SHA256: 8699ccc7479d81f24feaa118320085d986b3bbace3086f2d25f790b4da8b06a7 SHA1: 90cd298b2c43fa2794edd092ed38794496b124aa MD5sum: e26f0eaa3e6a00052b37d30019021d18 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: i386 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_i386.deb Size: 59308 SHA256: 61bda2c52b962de9e33a75688f65ad0dd0df6c958007eafe8f5922e19e7163af SHA1: 71ff48ea63b49aa5cc07fad0e80347a6ed0d725d MD5sum: 3cb3c3ffaaa7a470ecfe6c83ef6ca79f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 60 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_i386.deb Size: 18560 SHA256: 759db1d63b7dfce0eee4579e753934bd319d7d91d8bb97aded0cd501590fbe0b SHA1: 8a0187f130731affc7617e70d56c98fdc9ff6305 MD5sum: e790b677185dc6835e9ff64182759491 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 332 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_i386.deb Size: 132818 SHA256: 2cbf79084ef8a3e60638a6633fdb2b73a5dccf0f94c2db690586a33ede0201af SHA1: a0fb457e46863b863081cad73f0d430f649e607e MD5sum: 7f5a11aa5a57e1f2056acf49b75e8672 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2124 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.4), freeglut3, libasound2 (>> 1.0.22), libc6 (>= 2.7), libdc1394-22, libfreenect0.0, libgcc1 (>= 1:4.1.1), 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_i386.deb Size: 685072 SHA256: bf1445056f5442cba4c7cb75984356c89b4c8a1d5d7f0b4d4d642edc4d4a72c4 SHA1: 0abac92b8c7dbf7f7153410679aadebce94fc809 MD5sum: 57f84e5da0230b5df4c08a7616c0eb92 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: i386 Maintainer: NeuroDebian Team Installed-Size: 556 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_i386.deb Size: 152402 SHA256: 783356064d0921be805bb9c99ef62c6f48a210e4b39c6501a9fa3002eff59a64 SHA1: 5e22f941bd62323ab4d94da7db34036895e71df0 MD5sum: 1913d1041bc1eadff878a7839c546dd3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 13000 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), libopenthreads13 (>= 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_i386.deb Size: 4357246 SHA256: 71db2d8183df1e93f1e3c405dd845264bf8fe5072806dc3549b3f3f56ef1b2f8 SHA1: c8e7df49e72ade15499425c6087a21d4f861f283 MD5sum: cdf2ddcdc2cc0cebbecfecb2845dae39 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1776 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), libopenthreads13 (>= 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 (>= 4.2.1) 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_i386.deb Size: 608206 SHA256: f39fc518056b162d150fe52893c49f4ec6b9268c5365f210e15cb3c4579e797a SHA1: 5041324d594cc1f7e82b3b5a26009249414e03ed MD5sum: 3354d1938af98fa1de6e25f11e4b3005 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2240 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_i386.deb Size: 753802 SHA256: d46b72d9c45c540aa07efb20230fb16cffe4dcd7e5c99b99fb6e578ef69e6661 SHA1: 29797f13bc152c5c373d3b305cde096d4102dcc3 MD5sum: 182b63b57a60ec7cf7ceaae397a52545 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 176 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 59786 SHA256: 889cd868864ffd8c4487f7014567daf2e488b799ae075a442ed1cc1088bf815d SHA1: 9c9cb4b769b6a6e8a6afd9fd3050a94998ec1700 MD5sum: 91b4c600860813c7f98498eafa447a74 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 204 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_i386.deb Size: 50766 SHA256: 6c8b4fed2fd3145b3a4dbd5f8ed1dd12686b41855096a2b6321daf050365c2bc SHA1: 4a54be7921534f32e5486be48774a6428af71cf6 MD5sum: 5d6c26aa030a5eb11c1de38e4ea45ac3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 144 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_i386.deb Size: 51430 SHA256: e1ea93f75575d1eee8ed55cf25a16416afa2b6325ec0eef81e344efafe7295ff SHA1: d5f1cde8f9b988ddfb0dd804130c2a8656bcacec MD5sum: de14f98804050cd6d7522fa8e23532e9 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 540 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_i386.deb Size: 183640 SHA256: f9484cff678d8bf06945cd0854b0e196722ddf220d4b53a43d83f7644914b703 SHA1: b7d6c3ec4514169f88812de6aedb1eeef9121c7c MD5sum: e559eb273b65251839d2af6600f53746 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 112 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_i386.deb Size: 27456 SHA256: 86fa4c763ab0e56e6560924469a5f65d41fa20af0e1930676251e99443cca458 SHA1: 9a26de7d1ffedb28b4b5f084582d39441bc4e2c1 MD5sum: b60c8ae3b0a8b3420ac10720b7108ec5 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: i386 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_i386.deb Size: 7610 SHA256: 510e79068ac4b27dde95515c4ab13a708db7a2312c4971261eea75c0cb9eded2 SHA1: f99efc9bf50947a56547e64adade989a2c490a37 MD5sum: df23b04f158394e251b3610bdc2e95f4 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: i386 Maintainer: NeuroDebian Team Installed-Size: 288 Depends: libc6 (>= 2.3.6-6~), 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_i386.deb Size: 61960 SHA256: 69fd245c477437e07fad88bfc8448283b7dac70035a9d4036cb72122d3355f27 SHA1: e1cf93b4d9c32c486d07079351bab5336b9d572c MD5sum: 76626ea954c78fb83a4782061996ff41 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 984 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_i386.deb Size: 275740 SHA256: 8960c771b3e0e4d1c4a82e4ab757a24f85a2e8950e9d2a8626a0129c673b7267 SHA1: 5b063f35c6a1934b5161964e75162a9e6190e46c MD5sum: 614b99498a27633c292db082d78c3c1c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1364 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_i386.deb Size: 509746 SHA256: e660e27f1631226cfa3b4831ebe82a393fdfa2be92b04d68c7581eb115e9664f SHA1: ffaeb7cfb06408138ae558058e541abfa45d0e4b MD5sum: 4c0b91e89b09f9b494550f3a8c804690 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 116 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_i386.deb Size: 33974 SHA256: bc49fc3d9141a39df97b5070a89851542d1ffbc5369c37ebf6a3bf1c3028784c SHA1: 0f6a1a77a8c0133a71535949381561fa393b3cc5 MD5sum: 4c19b53b4490716a7511723ff1814aa9 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 116 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_i386.deb Size: 42498 SHA256: 2bb97c80811a381b1dbe60b4cf0aaee217783f75f9501d0e9ceb7ef43171aab3 SHA1: f9aeebc73420099392066d000ad7e437dc048251 MD5sum: 0636d5261166313a49f5446a7545130e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1084 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_i386.deb Size: 278174 SHA256: 061f3fb5602bee1ed96a2fb06a735aa2192c3eb6d60eb882a8b12864a8627801 SHA1: 780673523937dc0ba529f631659d145ac26d30b1 MD5sum: b515054978d2e99e7a248cb79b5467d0 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2092 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_i386.deb Size: 646814 SHA256: e314ebff4e5348fa2a52a5f1cc01c350d928dd206d32ddf80f9d6df55def7146 SHA1: 3617a0009b47ef33eebdca7244d39b9b0b45f0ec MD5sum: 2570da1ba78cda004f98a4cc31dd4c11 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2240 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_i386.deb Size: 706700 SHA256: 22c0ec741b2c24dc2c650960eff8c665447b54338fd49b0e57de982f82245a9f SHA1: ecd96deb3d3456477fb09badfd4e47a62ba09ac0 MD5sum: ade57cfd32596b8594cba398d87349eb 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: i386 Maintainer: NeuroDebian Team Installed-Size: 560 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_i386.deb Size: 146574 SHA256: ba0240b2c518087ddee3eb74e28f819a5d9d27d616cf33bdf2f654faf9837a71 SHA1: ab0544329617edd43b16af2b4ace8c56a18eac30 MD5sum: d7ccd32f13a73b2938663c511cdbad2a 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: i386 Maintainer: NeuroDebian Team Installed-Size: 1584 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_i386.deb Size: 348724 SHA256: e492fe000c55e4d56cf178e68c68c458b42b21fbe092d83db7f6465b38ea4b8b SHA1: 02d7343f7b494907b0739c48bf3a73e600238180 MD5sum: 06ea149b83d3e816ef5ecbe05339dc96 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1200 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_i386.deb Size: 438230 SHA256: 730b21eab88ae7d70d2b5083a28f3e8bffaa4b61cd1f84e066d1a676ce06a6da SHA1: 6ad03034d4604b833198721f3538ee1e35fc3d38 MD5sum: ee6e2bbab9269310d51ebb54e1580358 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 452 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_i386.deb Size: 191674 SHA256: c3e68056f8ff97917c3c3b41d776cc5b3a93dd58fae67cfb773f7fad9e965bdf SHA1: da5a9564210d9cde9a022d7870971008b6f75a71 MD5sum: 51f88461b8a590b2bda40de3c6fa66ca 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: i386 Bugs: mailto:bugs@neuro.debian.net Maintainer: NeuroDebian Team Installed-Size: 2096 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_i386.deb Size: 334866 SHA256: 8f97f237f4020f69f13ae055b1605807d14bae47ecfb31bf6a749b8c4e84e6d2 SHA1: ed5fc2068ff12f293d11b756ba6b2478123a02af MD5sum: 461f74d4f3af6e15e43c5064966209c3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 992 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_i386.deb Size: 431380 SHA256: 960631f5d92fbb721deec1ac3c5cc8640c9ae426b3ed7dcaf1c18f8a3ad39431 SHA1: 8a2b432a6f6b7a49020865dc603ba39f0cb61b58 MD5sum: 5b9ed3808afe379bc4a31828a4d75579 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1948 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_i386.deb Size: 714906 SHA256: 02ba29939a79bacd338540b8a06a819f1cd87faf8bc43e6e65e646cf1c5377f8 SHA1: 874c24f244065645b595584505ad094b97b7df92 MD5sum: eb97f50b37c6f00cceaf506ef190195c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 13112 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_i386.deb Size: 5013218 SHA256: 9dbf92f080b7e1729e5bda267a967e9424f796d419ad20144ba511abf80d8fcf SHA1: f2d974ddefe2c286646f3cb53bd764f406ba06d2 MD5sum: f9019c249f35aad529705b0df6281b05 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 9720 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_i386.deb Size: 3660158 SHA256: ade37c7eca516be5e09ca5071592788bd45c0658ac25858c0c3905df27d52311 SHA1: 84e5a08cf9eee95d8a98f2306dbc288fc05b1216 MD5sum: f9f05c0198efdcd3232d1453c172a364 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.