Package: aghermann Version: 0.9.1.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1431 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.8), libcairo-gobject2 (>= 1.10.0), libcairo2 (>= 1.2.4), libconfig++9, libfftw3-3, libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.31.18), libgomp1 (>= 4.4), libgsl0ldbl (>= 1.9), libgtk-3-0 (>= 3.3.16), libitpp7, libpango1.0-0 (>= 1.14.0), libsamplerate0 (>= 0.1.7), libstdc++6 (>= 4.6), libunique-3.0-0 (>= 2.90.1), libvte-2.90-9 (>= 1:0.27.2), libx11-6 Suggests: edfbrowser Homepage: http://johnhommer.com/academic/code/aghermann Priority: optional Section: science Filename: pool/main/a/aghermann/aghermann_0.9.1.1-1~nd70+1_amd64.deb Size: 642408 SHA256: 9827057440aaff9f52fc95c24d036cf7a6378514ec7314fd8541ecef845bb552 SHA1: 0ed2aa59906a19d96b3b5866f681598784a3d49c MD5sum: 73e51ce9f1a2f2a3a76ea0b50ac086b1 Description: Sleep-research experiment manager Aghermann is a program designed around a common workflow in sleep-research, complete with scoring facility; cairo subpixel drawing on screen or to file; conventional PSD and EEG Micrcontinuity profiles; Independent Component Analysis; artifact detection; and Process S simulation following Achermann et al, 1993. Package: ants Version: 1.9.2+svn680.dfsg-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 41892 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.20, libstdc++6 (>= 4.6) 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-3~nd70+1_amd64.deb Size: 12729054 SHA256: 516a2648d007e63c4de69c2784bbc3204bce6c3b5ee548ebefd562edd8a36f2c SHA1: a772e941b219c49d6f8ba0ea0fd1a606bcf0910b MD5sum: 8ae964829eaa5a0f1de06ca4241ef029 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: biosig-tools Source: biosig4c++ Version: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 661 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_1.4.1-1~nd70+1_amd64.deb Size: 280788 SHA256: cc488a2fb58d12968d95e9a2cc98f84e958feb760b6978a9e6d77c8b7181f852 SHA1: 6d017cc8def6a96fe1038392653d2c618b63b2ff MD5sum: d4bfb762be59061d61ecfa5e31f81135 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.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 18488 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), 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 (>= 4.6), libvtk5.8, zlib1g (>= 1:1.2.3.3) 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.1-2~nd70+1_amd64.deb Size: 7376026 SHA256: 18a7e2e54edf249436ed4f63dd73b3ca2642abf97e3f0a9581486abf65c8e32c SHA1: 23be5cb2697e9529f5b34e2e492dd578ba623d47 MD5sum: f484324687f5fde85147cfb3e892d3ca 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 information. Package: cde Version: 0.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 988 Depends: neurodebian-popularity-contest, libc6 (>= 2.3) Homepage: http://www.stanford.edu/~pgbovine/cdepack.html Priority: optional Section: utils Filename: pool/main/c/cde/cde_0.1-1~nd70+1_amd64.deb Size: 356878 SHA256: 3435a03ebc0bd6cecbf3d6bad539be0bae39d575c327e2bc7c8a93e168edfc69 SHA1: e8a256b6a47a28b80111cd535375853106ada2f0 MD5sum: 4457b40973d5e7fa0bf3cc4ae40167a5 Description: package everything required to execute a Linux command on another computer CDEpack (Code, Data, and Environment packaging) is a tool that automatically packages up everything required to execute a Linux command on another computer without any installation or configuration. A command can range from something as simple as a command-line utility to a sophisticated GUI application with 3D graphics. The only requirement is that the other computer have the same hardware architecture (e.g., x86) and major kernel version (e.g., 2.6.X) as yours. CDEpack allows you to easily run programs without the dependency hell that inevitably occurs when attempting to install software or libraries. . Typical use cases: 1. Quickly share prototype software 2. Try out software in non-native environments 3. Perform reproducible research 4. Instantly deploy applications to cluster or cloud computing 5. Submit executable bug reports 6. Package class programming assignments 7. Easily collaborate on coding projects Package: cgroup-bin Source: libcgroup Version: 0.37.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 162 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libcgroup1 Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: admin Filename: pool/main/libc/libcgroup/cgroup-bin_0.37.1-1~nd70+1_amd64.deb Size: 66046 SHA256: 6943e3e514829289c63c84f6112178c4bf02a24b7b6a31c688839b83aa659a59 SHA1: 904415be370d6ab952e17e040fadd0ef13a611e0 MD5sum: f11926de39d36d36c044fb12e6eb2da0 Description: Tools to control and monitor control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . These tools help manipulate, control, administrate and monitor control groups and the associated controllers. Package: cmtk Version: 2.2.6-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 23774 Depends: neurodebian-popularity-contest, libbz2-1.0, libc6 (>= 2.7), libcharls1, libdcmtk2 (>= 3.6.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.2.1), libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libsqlite3-0 (>= 3.5.9), libstdc++6 (>= 4.6), libtiff4 (>> 3.9.5-3~), zlib1g (>= 1:1.1.4) Recommends: sri24-atlas Suggests: numdiff Homepage: http://www.nitrc.org/projects/cmtk/ Priority: extra Section: science Filename: pool/main/c/cmtk/cmtk_2.2.6-1~nd70+1_amd64.deb Size: 6558608 SHA256: b90b866d0bbac7fbe0818e1e96c3ad37d17e5e409b80e736d725ed00fcf0c296 SHA1: ae006277e2dffed2f33a2c0a66583819323dd241 MD5sum: 0ca768b4e941a54a3053b3a25ea31807 Description: Computational Morphometry Toolkit A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O. . The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear regression). Package: cnrun Version: 1.1.13-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 495 Depends: neurodebian-popularity-contest, libboost-regex1.49.0 (>= 1.49.0-1), libc6 (>= 2.8), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libreadline6 (>= 6.0), libstdc++6 (>= 4.6), libxml2 (>= 2.6.27) Suggests: gnuplot Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun_1.1.13-1~nd70+1_amd64.deb Size: 186630 SHA256: a2416cd948df094d5bf8c75df604949023c8e2e0a02685cf5deec2a418f0ec38 SHA1: 426e4b80301d05f48ec993eb696778e630b4ea21 MD5sum: 983731612c5e1e7171d2bb8a61101772 Description: NeuroML-capable neuronal network simulator CNrun is a neuronal network model simulator, similar in purpose to NEURON except that individual neurons are not compartmentalised. It can read NeuroML files (e.g., as generated by neuroConstruct); provides a Hodgkin-Huxley neuron (plus some varieties), a Rall and Alpha-Beta synapses, Poisson, Van der Pol, Colpitts oscillators and regular pulse generator; external inputs and logging state variables. Uses a 6-5 Runge-Kutta integration method. Basic scripting and (if run interactively) context-aware autocompletion. Package: condor Version: 7.8.8~dfsg.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 14976 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.7), libclassad3, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-callout0 (>= 2), libglobus-common0 (>= 14), libglobus-ftp-control1 (>= 4), libglobus-gass-transfer2 (>= 7), libglobus-gram-client3 (>= 12), libglobus-gram-protocol3 (>= 11), libglobus-gsi-callback0 (>= 4), libglobus-gsi-cert-utils0 (>= 8), libglobus-gsi-credential1 (>= 5), libglobus-gsi-openssl-error0 (>= 2), libglobus-gsi-proxy-core0 (>= 6), libglobus-gsi-proxy-ssl1 (>= 4), libglobus-gsi-sysconfig1 (>= 5), libglobus-gss-assist3 (>= 8), libglobus-gssapi-error2 (>= 4), libglobus-gssapi-gsi4 (>= 10), libglobus-io3 (>= 9), libglobus-openssl-module0 (>= 3), libglobus-rsl2 (>= 9), libglobus-xio0 (>= 3), libgsoap2, libgssapi-krb5-2 (>= 1.6.dfsg.2), libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.10+dfsg~), libkrb5support0 (>= 1.7dfsg~beta2), libldap-2.4-2 (>= 2.4.7), libpcre3 (>= 8.10), libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.6), libuuid1 (>= 2.16), libvirt0 (>= 0.5.0), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), python, perl, adduser, libdate-manip-perl Recommends: dmtcp Suggests: coop-computing-tools Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/condor_7.8.8~dfsg.1-2~nd70+1_amd64.deb Size: 5047982 SHA256: e4af66041818a1866a18458dede3055ab611603dfc51750be9eed41dfbf977d7 SHA1: a68d39a92e7d8c9f958afba825ed5b9ccd86e29d MD5sum: 4c144ca872213a5d82d7609a6504e2fc Description: distributed 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 systems, 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 can set up an appropriate initial configuration at install time for a machine intended either as a member of an existing Condor pool or as a "Personal" (single machine) Condor pool. Package: condor-dbg Source: condor Version: 7.8.8~dfsg.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 35974 Depends: neurodebian-popularity-contest, condor (= 7.8.8~dfsg.1-2~nd70+1) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: debug Filename: pool/main/c/condor/condor-dbg_7.8.8~dfsg.1-2~nd70+1_amd64.deb Size: 12410034 SHA256: 69271ca54eed77029ca624dc4b549c9a118e4d2263d1dae5e04c88f07aa7924f SHA1: e837b6e982b3b6cf7a896eccf39cc71f832103b2 MD5sum: d455489ecd49b680b8b252446040a685 Description: distributed workload management system - debugging symbols 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 systems, 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-dev Source: condor Version: 7.8.8~dfsg.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 2067 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: devel Filename: pool/main/c/condor/condor-dev_7.8.8~dfsg.1-2~nd70+1_amd64.deb Size: 459656 SHA256: 3c9ca962e942263c91f0ee5944f0aaf4626e347e98e862d70985ba698fc14d7c SHA1: 951aef58091b671c01e53752524dd9d38a3ee320 MD5sum: 12001ac29225af79c2e634d7d7375f2d Description: distributed workload management system - development files 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 systems, 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 headers and libraries for development of Condor add-ons. Package: condor-doc Source: condor Version: 7.8.8~dfsg.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6121 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.8~dfsg.1-2~nd70+1_all.deb Size: 1466672 SHA256: 6a970eb963f109b4d91cf0f66127c5e8d6a8980fd83be5137231c3f752131a01 SHA1: 1459bdb70d907cb0e342437ad9029ba414c68309 MD5sum: 1a273d8944188d36b9eb029619118f9b Description: distributed workload management system - documentation 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 systems, 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: 2.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1578 Depends: neurodebian-popularity-contest, python (<< 2.8), 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 (>= 4.0.0), 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_2.1.0-1~nd70+1_all.deb Size: 1356156 SHA256: 84e3a8e4487cd67005eaf2c292b248e7e812057408ca7b7e012d71c3684298c2 SHA1: a20067603c1694d3c598d7e261e2bb64a98253df MD5sum: 4325ba9177d6224461c4520b1b7a41a0 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: 3.4.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4143 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), libfuse2 (>= 2.8.1), libglobus-common0 (>= 14), libglobus-gss-assist3 (>= 8), libglobus-gssapi-gsi4 (>= 10), libkrb5-3 (>= 1.6.dfsg.2), libmysqlclient16 (>= 5.1.50-1), libncurses5 (>= 5.5-5~), libopenmpi1.3, libreadline6 (>= 6.0), libstdc++6 (>= 4.1.1), libtinfo5, python 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_3.4.2-1~nd70+1_amd64.deb Size: 1385724 SHA256: 3e3d109bc2270fb3aaa50af892e1758c0a833bc2b4142041d24b58f8ec47bb9f SHA1: 2f24ce46104eb364890a38d7b3fb230dfaeb2eac MD5sum: 1cb17a03918c8b97568dc66fb129be01 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: 3.4.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 956 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: libs Filename: pool/main/c/cctools/coop-computing-tools-dev_3.4.2-1~nd70+1_amd64.deb Size: 223124 SHA256: 966931cea9c2772f2f274e8f28f56862ea3bb2ce7e75b925db29948b5a327135 SHA1: 7823084ce78245027b30988d842a5c2059714e33 MD5sum: 73c8560240dd6698aceb86886f1abe28 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: 3.4.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2319 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_3.4.2-1~nd70+1_all.deb Size: 310890 SHA256: ca1fc4a117105875244c5c1a16994aa4e1c7496de9d177e96bbd351def1da0b5 SHA1: 154b372d4c5b7a25d5885e2ae8d79e64808671b2 MD5sum: c5f2ca94795a12217de0438befa22e8d 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: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: debruijn Version: 1.6-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 144 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfftw3-3, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.cfn.upenn.edu/aguirre/wiki/public:de_bruijn_software Priority: extra Section: science Filename: pool/main/d/debruijn/debruijn_1.6-1~nd70+1_amd64.deb Size: 47854 SHA256: 834ad1dd20d8b12c483e3e6abf0c21a05985bc74575b068ba1d17dcd7392967e SHA1: 1fb9fbf9c85b92721515bc835966e4411466044c MD5sum: 15ea946e8d25cb946d78d24ab573601a 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: 2.30.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 584 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libnifti2, libstdc++6 (>= 4.6) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.30.0-1~nd70+1_amd64.deb Size: 183640 SHA256: 761e457151897843ae4fd7ea5e770aec331eca6e0835a691bf8af553e72b7866 SHA1: 802858c146ca35a709cb72743770252c3e8356a8 MD5sum: 804ff203029887944217bb947d82b07d 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: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2018 Depends: neurodebian-popularity-contest, libmtcp1, libc6 (>= 2.9), 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_1.2.5-1~nd70+1_amd64.deb Size: 860466 SHA256: 0a480f8ee1cb5dd030bb834b6c8ed8ddfe433325f758e9ae70b10fcaa836e819 SHA1: 36def52d578664b618870786c342100eeb961b67 MD5sum: 567990655954084668cab13dfe064152 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-Window 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: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 19302 Depends: neurodebian-popularity-contest, dmtcp Homepage: http://dmtcp.sourceforge.net Priority: extra Section: debug Filename: pool/main/d/dmtcp/dmtcp-dbg_1.2.5-1~nd70+1_amd64.deb Size: 5342814 SHA256: 85b50d261f36d89759ff5bb47f085232cae31e5c3b4c9d17058f5f92372ad060 SHA1: dcd7bcd79986893dc073fa2d4792bfc4427bcfc4 MD5sum: 87a68963204be00919343ee06b3fae17 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-Window applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package contains debugging symbols for DMTCP. Package: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 55 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libedac1, lsb-base (>= 3.0-6) Recommends: dmidecode Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: admin Filename: pool/main/e/edac-utils/edac-utils_0.18-1~nd70+1_amd64.deb Size: 29430 SHA256: 988d46cb01ef63b5f373dd756b9e0c550cd90cd55aeec1b4f48c8ef4e09338b5 SHA1: e6f070ab8993ab02d714936df3c8d4f99361e541 MD5sum: 4adcfc104be6d115e60f64aa39e2689b Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package provides command lines tools Package: eegdev-plugins-free Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 83 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libeegdev0 (= 0.2-3~nd70+1), libc6 (>= 2.3.4), libexpat1 (>= 2.0.1), libusb-1.0-0 (>= 2:1.0.8), libxdffileio0 (>= 0.0) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: libs Filename: pool/main/e/eegdev/eegdev-plugins-free_0.2-3~nd70+1_amd64.deb Size: 28728 SHA256: 836e4c8f55206d8ba1f4440c05a7adbdf7582513bdc8561caac85ef475a7b618 SHA1: 3ac001b19c6df3cf304066cbd952a89cf90fd1fd MD5sum: f3264511bd8d6984c73c23c15014229e Description: Biosignal acquisition device library (free plugins) eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package contains the devices plugins that depends only on free components. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd70+1_all.deb Size: 7224720 SHA256: a25c47daa7e5cabbab1e2864994d7ca0d5b207e5609c31fe0f62c32fae733590 SHA1: 6a5b78425b50d335c0f1e49bc20cd68aae0ab3fc MD5sum: fdcfc99b0c53436258c20f5eee125e50 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: eegview Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 22 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libeegdev0, libmcpanel0 (>= 0.0), libxdffileio0 (>= 0.0) Homepage: http://cnbi.epfl.ch/software/eegview.html Priority: extra Section: science Filename: pool/main/e/eegview/eegview_0.0-1~nd70+1_amd64.deb Size: 13090 SHA256: 0cd9b3391951f0b54841cc3f4cadf3acd9069f94518f9e9629f75d63340cc37b SHA1: f0450b1a88c93694bd1d23ce2bbb25a8d4a6ebd1 MD5sum: 55e8a7fe33d248c3d17c4d9332f1ffab Description: Software to display EEG data in realtime This software allows one to display EEG signal in realtime as well as record them. It is the minimal recording panel needed to do simple experiment. Package: fail2ban Version: 0.8.10-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 402 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.10-1~nd70+1_all.deb Size: 134644 SHA256: b270e6428fce15c999820a5070dee2bb378bda6e692c4f5bb77aba84e93249ac SHA1: 2291b9564907b925c69f87cb11cfa1e62dbfd1ab MD5sum: 6aa69374223086e9846c9795317252a2 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. Package: freeipmi Version: 1.1.5-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd70+1_all.deb Size: 932 SHA256: d052644b2bb73930387779f71d65b14b041c033fb7624b1622b3a5b8f7697f8c SHA1: 09e5ab7649c67b3f40276828fb0642fcdf5eb6ba MD5sum: a5c30bc228c954949bd8be2e8beff792 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This meta-package depends on all separate modules of freeipmi. Package: freeipmi-bmc-watchdog Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 264 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreeipmi12 (>= 1.1.5), libgcrypt11 (>= 1.4.5), freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-bmc-watchdog_1.1.5-3~nd70+1_amd64.deb Size: 194736 SHA256: a12d808a512c196add94c3c8f28a69a09e72ea55b2061102f9affb3f415bc002 SHA1: fce701155bdedcdab6563e0b78fab736403ca565 MD5sum: 575fbfeceb73139cd9ae78b6a9bbb9fe Description: GNU implementation of the IPMI protocol - BMC watchdog FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package contains a watchdog daemon for hardware BMC watchdogs. Package: freeipmi-common Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 380 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd70+1_all.deb Size: 296952 SHA256: 8065c241cbdd833fab835d8944d5dca4c060573142745b81652ac0ac9000adfd SHA1: c508dc59367a85942fa9b41e3a58587e7c307c5c MD5sum: f78d625118a54dc59c26b3a89c6db926 Description: GNU implementation of the IPMI protocol - common files FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package provides configuration used by the rest of FreeIPMI framework and generic documentation to orient the user. Package: freeipmi-ipmidetect Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 235 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreeipmi12 (>= 1.1.5), libgcrypt11 (>= 1.4.5), libipmidetect0 (>= 1.1.5) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-ipmidetect_1.1.5-3~nd70+1_amd64.deb Size: 189008 SHA256: c1827952a05dc1d10be71c0b1c78a65fd1f46b4dde9afc20639fbe3b554cb6e2 SHA1: 7e039c971e5622313646b59dd066ce595dd96670 MD5sum: 2f30dc87e4344d0c8edee8e09580324b Description: GNU IPMI - IPMI node detection tool FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package contains a tool and a daemon for detecting IPMI nodes. Package: freeipmi-tools Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3265 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreeipmi12 (>= 1.1.5), libgcrypt11 (>= 1.4.5), libipmiconsole2 (>= 1.1.5), libipmidetect0 (>= 1.1.5) Suggests: freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-tools_1.1.5-3~nd70+1_amd64.deb Size: 1567180 SHA256: 58280543af212d3da7bda9cbc26c8b70fd4ff08bb7729b449c642255bacdb63e SHA1: 053ce0494d09441a2713c72b416ddeb57f4133e9 MD5sum: 92a07c1a901a267b34bae8e7cbaa15ad Description: GNU implementation of the IPMI protocol - tools FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package contains assorted IPMI-related tools: * bmc-config - configure BMC values * bmc-info - display BMC information * ipmi-chassis - IPMI chassis management utility * ipmi-fru - display FRU information * ipmi-locate - IPMI probing utility * ipmi-oem - IPMI OEM utility * ipmi-raw - IPMI raw communication utility * ipmi-sel - display SEL entries * ipmi-sensors - display IPMI sensor information * ipmi-sensors-config - configure sensors * ipmiconsole - IPMI console utility * ipmiping - send IPMI Get Authentication Capabilitiy request * ipmipower - IPMI power control utility * pef-config - configure PEF values * rmcpping - send RMCP Ping to network hosts Package: freenect Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, libfreenect-bin, libfreenect-dev, libfreenect0.1, libfreenect-doc Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/freenect_0.1.2+dfsg-6~nd70+1_amd64.deb Size: 7328 SHA256: b5044a9ffb1832c0b245b925bf0cf35051b29760fcf956a4b2b2cfe67b9ed0dc SHA1: 563d379204853b21991c8db2bec9daee2df98d9b MD5sum: 39b3b32066b1596821c1929afd341608 Description: library for accessing Kinect device -- metapackage libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This is the metapackage to install all components of the project. Package: fslview Version: 4.0.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6632 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt4-qt3support (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.7.0~beta1), libqwt5-qt4, libstdc++6 (>= 4.6), libvtk5.8, libvtk5.8-qt4 Recommends: fslview-doc, qt-assistant-compat 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_4.0.1-2~nd70+1_amd64.deb Size: 2386376 SHA256: efa181325dd3454b10e42137c12c733a3574ed8c9cfe10d815b6e5b3f2ea045e SHA1: 5577f285a50c37a50329cedf43065c1dfa1c1b8d MD5sum: 36099902e710ebff96918011825d433f 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: 4.0.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd70+1_all.deb Size: 2346532 SHA256: 485b06c824c12100413729f1a795e7963897b84bf9b92e2b8b91b5f207f1e709 SHA1: fe14f20c5faf550a29cb9b24121fc2cfddedacd1 MD5sum: 641476378b45b8e334faefe1ccdff8cb Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gdf-tools Source: libgdf Version: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 181 Depends: neurodebian-popularity-contest, libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-program-options1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.1.1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: utils Filename: pool/main/libg/libgdf/gdf-tools_0.1.2-2~nd70+1_amd64.deb Size: 55060 SHA256: 355f4069983473e95ee8ea106a75b681d8f6834ac6525f6ffc5a82348c0bae25 SHA1: f9c76ca93b63b84af5f9564c672ffa917f990fce MD5sum: 537eacc7f40107f5427640ef53cd7ef2 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: glew-utils Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 426 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd70+1), libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libx11-6, libxext6, libxi6, libxmu6 Replaces: libglew1.6 (<< 1.7) Homepage: http://glew.sourceforge.net Priority: optional Section: utils Filename: pool/main/g/glew/glew-utils_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 135510 SHA256: 0d75203ae8729dbdbc8fe4863cc2f4df1800c6ccb93a7b34da45d70d3caf1feb SHA1: 7c75495dff3565257df62d2449a9210083f6f861 MD5sum: 58c11432c32f4f0d68b579ffee66d604 Description: OpenGL Extension Wrangler - utilities For more information about GLEW please refer to the description of the libglew-dev package. . This package contains the utilities which can be used to query the supported OpenGL extensions. Package: gmsl Version: 1.1.3-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.3-2~nd70+1_all.deb Size: 16312 SHA256: 81fa733b729f71a9b6598c572347e092fb1daba4b7a98ed5cc765aee3ee44a2f SHA1: eafa70ea9e88be816f77e1f4d7c3a0bedcb5476e MD5sum: b8fe56193108337856824bed0406a597 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 302 Depends: neurodebian-popularity-contest, guacd (>= 0.6), guacd (<< 0.7) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.6.0-1~nd70+1_all.deb Size: 277650 SHA256: a50628f5b19e7baa2256bf86499957f7c20fb920c2f9b66d51605d3dd24f7015 SHA1: 20433956b4e76f6b1592da48efe86be68ec817f9 MD5sum: 8eff61e47ae8dd6ba2a80951ef283625 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd70+1_all.deb Size: 5176 SHA256: 48da6fb47245d1fc57bd3f142db9cdb418b00bfd5974d948917e14faaa13a756 SHA1: 4810336460c661e65fdc5801a3358efd56603434 MD5sum: c844736c4586d9a1af28d3f58ba0a8dd Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: guacd Version: 0.6.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest, lsb-base (>= 3.0-6), libc6 (>= 2.2.5), libguac3 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacd/guacd_0.6.0-1~nd70+1_amd64.deb Size: 11780 SHA256: 2ad61521a6456ce46904332cad8d565ed9b37d3b337c4fba25524c0aec702a31 SHA1: 1cc9843b52475fdb22d2c4a7450bd6a2e4fc9b9c MD5sum: bb3875a628bdce52b022dbb14f8e284b Description: Guacamole proxy daemon The Guacamole proxy daemon, guacd, translates between remote desktop protocols (like VNC) and the Guacamole protocol using protocol plugins. Once a user is authenticated with the Guacamole web application, a tunnel is established through the web application to guacd, allowing the JavaScript client to communicate to an arbitrary remote desktop server through guacd. Package: impressive Version: 0.10.3+svn61-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2), perl Recommends: pdftk Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.3+svn61-1~nd70+1_all.deb Size: 155578 SHA256: 3546e182db03da8186d7c6afe785210102091aed423968250e260dc1f075f12e SHA1: 6467c36ac47b1993c3dc487a943ef0dfc45beedf MD5sum: 0600a7c30074b40da9a0d9207f1d6d04 Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd70+1_all.deb Size: 9652 SHA256: fac3ad8fc2cf1126a2b7fd3a9497594c3372cf7ae5a006d552d0b18e97334a11 SHA1: 803b8e967a16602928187f76ba0a8813d6a68866 MD5sum: c70545ff21713e721dbd16f9a195cbde Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.2.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2677 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.2.1-2~nd70+1_all.deb Size: 2408076 SHA256: 29e82b1719fa11a33cda9faa400bc033c0ee8241c9d9917e01f6d959ad5ce7e9 SHA1: 15ff635e7fb34b9052290258ec8b376a11d06f0c MD5sum: 8e4aa23ac9731dec02e834515eb4d260 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4808 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd70+1_all.deb Size: 1306320 SHA256: d259e419c42ab2f29c62a358f1b70ac483246c60043a213cf2a0e2ebb27940b9 SHA1: f1da0836b718381b16709910018994a049da53cd MD5sum: 445c27ebd25688a209351c5432f11a9b Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16664 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd70+1_all.deb Size: 7243134 SHA256: a34015da70830de42c97645c790f2fdc179da0b1b48848617dd8926b23b017e2 SHA1: 50455b67f63f0e2b7b95c4cda4c6f61feb14fa09 MD5sum: 4c412f1cfd211f9b4a81a0f7986b445f Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd70+1_all.deb Size: 896 SHA256: e6bf753904ea6c85c72689ffbe60b4f7b77243e38733c4c8a486c9b6fdeb69cd SHA1: 9226720c79cf6b2fecae5206e4a5af313318d950 MD5sum: 587920ae0a922c5a9ea5d60f75c52367 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd70+1_all.deb Size: 824 SHA256: 0097d83205fc332bebc5e9e178063ab3c6d740909a6c8ce7da2930d300556864 SHA1: ced489b459fa0edfd0a0414d2a0b4cac6cd7e9a8 MD5sum: 172195f46a65a28d182025cd62cd2503 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd70+1_all.deb Size: 910 SHA256: 009e2f9b28f70112713dfd1fa64bff7958a250fc2d5f622ef925c49d15afa5a1 SHA1: fb211e7d7981402a4329181ed727148ee38195d4 MD5sum: 9bb764488392203162c98cee5d3f794d Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: isis-utils Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 941 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmuparser0debian1, liboil0.3 (>= 0.3.1), libstdc++6 (>= 4.6) Homepage: https://github.com/isis-group Priority: extra Section: science Filename: pool/main/i/isis/isis-utils_0.4.7-1~nd70+1_amd64.deb Size: 271212 SHA256: 4017f4a3e56c998b5f276c39ed371cd04b8140907540be37d606ef40aea7376f SHA1: 5c685ea0a27a05eb1a29f22bfecece60e9ca6a2d MD5sum: 8c989e2f0ed79484796e7667374cfcd8 Description: utilities for the ISIS neuroimaging data I/O framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides a number of utilities to process neuroimaging data. This includes a multi-format converter and tools to inspect image meta data. Package: klustakwik Version: 2.0.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 92 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), 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~nd70+1_amd64.deb Size: 23078 SHA256: 408ce54522f5db716c99edea2089993436feb23cef17c2dfa7766beea8b76a68 SHA1: 0287982a5f922663215618f48730f7b209f7f7c9 MD5sum: 93df9b567b8a475c135145bc332a5656 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: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1695 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd70+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_1.4.1-1~nd70+1_amd64.deb Size: 422322 SHA256: 3b7b4e8751758f29d2ca8b6c3617c909872ed2d980c6a7ac86368d697380b826 SHA1: ca91389364ff1606e8b1923307266f6b9e86dd17 MD5sum: 73d99cfe8e307fa68edb23ba0b900eff 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: libbiosig1 Source: biosig4c++ Version: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 911 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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: libs Filename: pool/main/b/biosig4c++/libbiosig1_1.4.1-1~nd70+1_amd64.deb Size: 337398 SHA256: 3bcdc69d9c3625caa02da570b7c80161456543878aedb0b462a6e0b3740f38b7 SHA1: 14537aa8477fbc9e6affe8fb81171205d53ef948 MD5sum: 3c362fe33181e5e4d2751b2b62c7cd07 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: libbiosig1-dbg Source: biosig4c++ Version: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 377 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd70+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig1-dbg_1.4.1-1~nd70+1_amd64.deb Size: 118358 SHA256: 294cac7a098af8c345711c5a1392ac5c06f449d579a315406dd68089590d7c9b SHA1: 71c5bafdaa59ebc71008238dd4b6f7169afab1c9 MD5sum: 27c9b6df55d50f79d5e4f123a21d81db Description: I/O library for biomedical data - debug symbols BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides debug symbols. Package: libcgroup-dev Source: libcgroup Version: 0.37.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 51 Depends: neurodebian-popularity-contest, libcgroup1 (= 0.37.1-1~nd70+1) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libdevel Filename: pool/main/libc/libcgroup/libcgroup-dev_0.37.1-1~nd70+1_amd64.deb Size: 17398 SHA256: 9b70596ab471f2ab23d987d531483f1af36055bc5d8d4772a5545aa8081811c7 SHA1: e324f022fe2b3482fbf00beddb40146569f9b644 MD5sum: 4a13cc06d0cde6bc9aed2b5c4c837380 Description: Development libraries to develop applications that utilize control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . It provides API to create/delete and modify cgroup nodes. It will also in the future allow creation of persistent configuration for control groups and provide scripts to manage that configuration. Package: libcgroup1 Source: libcgroup Version: 0.37.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, libc6 (>= 2.7) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libs Filename: pool/main/libc/libcgroup/libcgroup1_0.37.1-1~nd70+1_amd64.deb Size: 38540 SHA256: 0bcf895f197ed19172cc3998452ef5d7c1b2fea61d68f385557d957a70e09047 SHA1: 3d877f31553b67cbac17086cb5ddc48aef394ad8 MD5sum: 8fefbb087322d3ca99c0d7bd5957c707 Description: Library to control and monitor control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . This library allows applications to manipulate, control, administrate and monitor control groups and the associated controllers. Package: libclassad-dev Source: condor Version: 7.8.8~dfsg.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 2834 Depends: neurodebian-popularity-contest, libclassad3 (= 7.8.8~dfsg.1-2~nd70+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: libdevel Filename: pool/main/c/condor/libclassad-dev_7.8.8~dfsg.1-2~nd70+1_amd64.deb Size: 525036 SHA256: 3e618d641f44ae78b33028d93e685acd4bb8274b4a900614c4da09f66d3a0820 SHA1: f500aa37902beee02035dd6baa75a05da2713e64 MD5sum: acdb404427278dc512b4ca67f5bbdde3 Description: Condor classads expression language - development library Classified Advertisements (classads) are the lingua franca of Condor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which 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: libclassad3 Source: condor Version: 7.8.8~dfsg.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 928 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libpcre3 (>= 8.10), libstdc++6 (>= 4.6) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/libclassad3_7.8.8~dfsg.1-2~nd70+1_amd64.deb Size: 285798 SHA256: c484f6ebc857703bf4786426027e1408badab2990a0050cf378e8c145bcba2dc SHA1: c90f3b0ed35f5a878828ed35b9c812261d64990a MD5sum: 5537d74f0b479018a57f681fc8f33e4f Description: Condor classads expression language - runtime library Classified Advertisements (classads) are the lingua franca of Condor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which 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: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest, libdmtcpaware1 (= 1.2.5-1~nd70+1) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/d/dmtcp/libdmtcpaware-dev_1.2.5-1~nd70+1_amd64.deb Size: 7116 SHA256: 1e11c37aa39b18b267ec0a22fc1558345abc35d68987d3d8eaffdbb1731577fd SHA1: edba98ee173c4443bad08c28fd68b11e79916dc6 MD5sum: 4539e55d0bc18e29412b313ac19ff803 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-Window 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: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15 Depends: neurodebian-popularity-contest, dmtcp, libc6 (>= 2.2.5) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libdmtcpaware1_1.2.5-1~nd70+1_amd64.deb Size: 7178 SHA256: b1b684b8e5a00c74daf03b22562de8f62b848351dfdee21b6b570076d6aa35fe SHA1: 14cb91c8f0ed5205f37622adb930c108da3afb05 MD5sum: 3a98a1ec607005792cb722662ffbc0ef 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-Window 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: libdrawtk-dev Source: drawtk Version: 2.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, libdrawtk0 (= 2.0-2~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libdevel Filename: pool/main/d/drawtk/libdrawtk-dev_2.0-2~nd70+1_amd64.deb Size: 43592 SHA256: b9ed91474f944d36ce4c32a19c86395a334bbcb53810d2e8bf27e345d4d91a75 SHA1: 1d54cad3a594b00e71c63d0bc3b504877c64eb1b MD5sum: 2625bc1927e69418f5178569a2ed05a8 Description: Library to simple and efficient 2D drawings (development files) This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL allowing fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. . This package contains the files needed to compile and link programs which use drawtk. Package: libdrawtk0 Source: drawtk Version: 2.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 66 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfontconfig1 (>= 2.9.0), libfreeimage3, libfreetype6 (>= 2.2.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.25), libsdl1.2debian (>= 1.2.11) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libs Filename: pool/main/d/drawtk/libdrawtk0_2.0-2~nd70+1_amd64.deb Size: 35678 SHA256: 4de278f86722f70c73e28e6de17b0994340367a5af09c8bfd369bd1c3c7d6c4d SHA1: 1011317660c481167f8fc3f2cd5f664e4e742c29 MD5sum: 11c462aea8bba5f7f3966e2bba5a6d04 Description: Library to simple and efficient 2D drawings This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL allowing fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. Package: libdrawtk0-dbg Source: drawtk Version: 2.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, libdrawtk0 (= 2.0-2~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: debug Filename: pool/main/d/drawtk/libdrawtk0-dbg_2.0-2~nd70+1_amd64.deb Size: 64094 SHA256: a7a9aa3d793255265cbc76f50946f235c12ceec4f5aada9d9035e4fd760d2aac SHA1: 3a32c6290c276c60a4507509fdbde3db1de72c8b MD5sum: e1abd9cd8085b59a3294625b7845a4b9 Description: Library to simple and efficient 2D drawings (debugging symbols) This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL allowing fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. . This package provides the debugging symbols for the library. Package: libedac-dev Source: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, libedac1 (= 0.18-1~nd70+1) Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: libdevel Filename: pool/main/e/edac-utils/libedac-dev_0.18-1~nd70+1_amd64.deb Size: 18998 SHA256: 0c6768e4e88205c8ac7bd3d32a571ab88ed0a2991131163134693e9ab0df048e SHA1: 173c8ae845418e377c6a14a4757024908c9da8e4 MD5sum: 99ff79c80af866929fe2461bf488ded8 Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package contains development files for the library Package: libedac1 Source: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libsysfs2 Provides: libedac Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: libs Filename: pool/main/e/edac-utils/libedac1_0.18-1~nd70+1_amd64.deb Size: 14990 SHA256: 3ca056758eb752f400ce3e498b075b32b5560aeecb4bd8a6b5203287191c6106 SHA1: a9d8cf91b6055ff7ff7e44cd5a233b22ef7f1024 MD5sum: f11364b55608db2d0ce50e9a2d684d84 Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package includes shared library Package: libedac1-dbg Source: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libedac1 (= 0.18-1~nd70+1) Provides: libedac Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: debug Filename: pool/main/e/edac-utils/libedac1-dbg_0.18-1~nd70+1_amd64.deb Size: 33438 SHA256: b2847b4ffec34d3a152113d7b94b95e963adfe51ea8ec0bf2a6ef4093b69b0d2 SHA1: defa9fd481f8f7082f3c761be3787f5ac322a100 MD5sum: be6a39e8861765ddd1d58aa781c961b1 Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package includes shared library with debugging symbols not stripped Package: libeegdev-dev Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 47 Depends: neurodebian-popularity-contest, libeegdev0 (= 0.2-3~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: libdevel Filename: pool/main/e/eegdev/libeegdev-dev_0.2-3~nd70+1_amd64.deb Size: 22434 SHA256: f7637b0e52661b8441133d6925c47def9eca334fe94b794a2d91bef9684e48ad SHA1: 2c9709328f5dfa1c55dd362c332c7638d3e87731 MD5sum: 5c9256c9af42dfdbf9d8c991795fbf68 Description: Biosignal acquisition device library (Developement files) eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package contains the files needed to compile and link programs which use eegdev. Its provides also the headers neeeded to develop new device plugins. The manpages and examples are shipped in this package. Package: libeegdev0 Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 80 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.3.2) Recommends: eegdev-plugins-free Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: libs Filename: pool/main/e/eegdev/libeegdev0_0.2-3~nd70+1_amd64.deb Size: 45012 SHA256: 57ed4a80b199995c051f7ec75e4bd13a053b445714cc86cbbd46f94919bbf01d SHA1: c49e563b9ce3a5036db6733d17c5294187a37389 MD5sum: b2f1601746465bc19b1cb601dfde021b Description: Biosignal acquisition device library eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package contains the core library Package: libeegdev0-dbg Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 167 Depends: neurodebian-popularity-contest, libeegdev0 (= 0.2-3~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: debug Filename: pool/main/e/eegdev/libeegdev0-dbg_0.2-3~nd70+1_amd64.deb Size: 141626 SHA256: e468d27bac7ec9ce64199276604e9db4e26fb476f9b6a79d8332936abb941c5e SHA1: 60e5088b5a1919d66432776737a1958ad4d83efd MD5sum: e9bec57f87b66369c7b1cb71afd6ad26 Description: Biosignal acquisition device library (Debugging symbols) eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package provides the debugging symbols for the library. Package: libeigen3-dev Source: eigen3 Version: 3.0.1-1.1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3412 Depends: neurodebian-popularity-contest Suggests: libeigen3-doc Homepage: http://eigen.tuxfamily.org Priority: extra Section: libdevel Filename: pool/main/e/eigen3/libeigen3-dev_3.0.1-1.1~nd70+1_amd64.deb Size: 509864 SHA256: 9d12cf00824b926884b6259eea04aad3c85b603603b490a96d0ff4ce45064756 SHA1: fbce8e2f0c2bcb9306b10abf8d53c2fffbc36fca MD5sum: 28d181ca81e4c367057b63a98f8761d4 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~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10344 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~nd70+1_all.deb Size: 2377384 SHA256: a49fd82e5f6a6d048154bd60d83245d840e38ec31ca1c90607c04479eaf6f04a SHA1: 551f098e9a8eae57dc8ac6baceb92ff5c87871e9 MD5sum: aefa7c3d5f3f5bfd5e3a481d932d7477 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: libfreeipmi-dev Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6782 Depends: neurodebian-popularity-contest, libfreeipmi12 (= 1.1.5-3~nd70+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libfreeipmi-dev_1.1.5-3~nd70+1_amd64.deb Size: 1279730 SHA256: 4c46ac06864958e072b37a3fa3d87aecdf92f4c9330d3beffe7349da3632d517 SHA1: 1c8667601e13ad46d0ec3abaebe0609f2961c32d MD5sum: e98275af7da4cf0037e1ccbebc84b3f4 Description: GNU IPMI - development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libfreeipmi. Package: libfreeipmi12 Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4625 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcrypt11 (>= 1.4.5), freeipmi-common (= 1.1.5-3~nd70+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libfreeipmi12_1.1.5-3~nd70+1_amd64.deb Size: 1101074 SHA256: bccd5585424f3f555842a785001a1d4cb81481bc327ba9c8eb8a415d6800263a SHA1: f0a8f731d1f7a3e374f741e651d998a4f27d3444 MD5sum: b56b0a15f23b98cab71767390e76ea16 Description: GNU IPMI - libraries FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . OpenIPMI, KCS, SMIC, SSIF, LAN drivers, and an IPMI API in a C Library. Package: libfreenect-bin Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 95 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.4), libfreenect0.1 (>= 1:0.1.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.1.1) Breaks: libfreenect-demos (<< 1:0.1.2+dfsg-1) Replaces: libfreenect-demos (<< 1:0.1.2+dfsg-1) Homepage: http://openkinect.org/ Priority: extra Section: utils Filename: pool/main/libf/libfreenect/libfreenect-bin_0.1.2+dfsg-6~nd70+1_amd64.deb Size: 38166 SHA256: 34c79e3e99480598cd629c4c1ec7259a4f3b7347afb880484dfa9bfae9e5f6b5 SHA1: aaeb3327867b5b23a4d4ea172c9d31b122e59d61 MD5sum: 4651263b5991fd82da058a0dc8ee7f8f Description: library for accessing Kinect device -- utilities and samples libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package includes utilities and sample programs for kinect. Package: libfreenect-demos Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, libfreenect-bin Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-demos_0.1.2+dfsg-6~nd70+1_amd64.deb Size: 7352 SHA256: 469a6ea182fe3f4dbfafd4990462ae2a5591c1a55383265048de06b5ba6c78c0 SHA1: 08b22745dcdd301b5665e2c6d6c3f16c3247b93c MD5sum: 500fe1521b189f14620f632accee5f66 Description: library for accessing Kinect device -- dummy package libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package is a metapackage to do the transition from libfreenect-demos to libfreenect-bin. This package can be removed after installation. Package: libfreenect-dev Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libfreenect0.1 (= 1:0.1.2+dfsg-6~nd70+1) Multi-Arch: same Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-dev_0.1.2+dfsg-6~nd70+1_amd64.deb Size: 17438 SHA256: d02410f1fb52a607064f8f7f377eab75e1706b99a1fbccf4c87a9438c53a94b3 SHA1: 8a6141c09c9667e5b4f306ba57c6ff95a91a12b6 MD5sum: f03d007d7367b33d8cd872966d4994a5 Description: library for accessing Kinect device -- development files libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This is the development package containing the libraries and header for software development with libfreenect. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 482 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd70+1_all.deb Size: 90828 SHA256: 23e2aa088d04437e5bb29a125098e50c02306c00bea9473ecfb05fb103d8a81b SHA1: 5d52d5550dc4b2558540c4cfa22479fd6e92c908 MD5sum: 36d9e1dd9960866bef703a872163d9a0 Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libfreenect0.1 Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 95 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libusb-1.0-0 (>= 2:1.0.8) Conflicts: libfreenect Multi-Arch: same Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.1_0.1.2+dfsg-6~nd70+1_amd64.deb Size: 37326 SHA256: 9b9b4ab0e3b082ae8cad872a1f1b64d9dc7080bcebde303f77c8b21f7e4c55e5 SHA1: b98c27ea41b699387dc41b17d8cbbc39d1d4034f MD5sum: 16d2de34d4596452ab503d083fdeefc8 Description: library for accessing Kinect device libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the shared library of libfreenect. Package: libgdf-dev Source: libgdf Version: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 97 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.2-2~nd70+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libdevel Filename: pool/main/libg/libgdf/libgdf-dev_0.1.2-2~nd70+1_amd64.deb Size: 19770 SHA256: 49e0f5a48d6a13bb15ea6429e7830b1b027b9487d5de5f77a29ef7218fc6b5da SHA1: ad0c3299900360cae38db590fe3b6b90dbb4d033 MD5sum: 0445434c686809a61771fae14c37878e 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: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 802 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libs Filename: pool/main/libg/libgdf/libgdf0_0.1.2-2~nd70+1_amd64.deb Size: 215642 SHA256: 469da38ea2fe770668a4da33af5377aaa0754a41da1cc305753a3324e35c2c65 SHA1: 714853b4df5b9cab988ccb2db1bc0b586e5dfd01 MD5sum: 555bcb37c448b89f1f7fcf1da7412188 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: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2169 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.2-2~nd70+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: debug Filename: pool/main/libg/libgdf/libgdf0-dbg_0.1.2-2~nd70+1_amd64.deb Size: 533706 SHA256: 4744ae089d8c0517e9a30e06f321171223e329d195a4e855375e1374fba34bda SHA1: 76fc35326e58936043e6c29c7d6edc18b14d07a6 MD5sum: c76109515596cf0059c53cde25ff1bb7 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: libglew1.9 Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 650 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libx11-6, libxext6, libxi6, libxmu6 Suggests: glew-utils Conflicts: libglew1 Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libs Filename: pool/main/g/glew/libglew1.9_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 183544 SHA256: 0d876aaa23812e4f9deef0c811a80da7c48aff1da4ad32bd892469e44a1957dc SHA1: bbf9a44448d0de7a762013201a56586c5c75cadf MD5sum: 53aa6d5ea36c16562c1a57cdb8842f44 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: libglew1.9-dbg Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 149 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd70+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglew1.9-dbg_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 41050 SHA256: b42b58d0bf3125e0d092b13b6e8895959d3ca758a776ad68e1f1f75d191010b6 SHA1: 6601359f829eb6211ad83999be815b2f82e69da4 MD5sum: a01ac1e09bcc416e96959da36bf6e78a Description: OpenGL Extension Wrangler (debugging symbols) 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 (http://www.opengl.org/registry). . This package contains the debugging symbols for libglew1.9. Package: libglew1.9-dev Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1000 Depends: neurodebian-popularity-contest, libgl1-mesa-dev | libgl-dev, libglew1.9 (= 1.9.0-3~bnd1~nd70+1), libglu1-mesa-dev | libglu-dev Conflicts: libglew-dev, libglew1.6-dev Provides: libglew1.5-dev, libglew1.6-dev Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/g/glew/libglew1.9-dev_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 153184 SHA256: d858c48eabe2b6c73291897479415378a515d3fcd5fe3d3a5910f761bbe84531 SHA1: 7b47290310c401a1d5ccd2a06668234a0958905b MD5sum: 89cd0a5eea2cfda2152f3113c76b6f8f 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 (http://www.opengl.org/registry). . This package contains the development documentation as well as the required header files. Package: libglewmx1.9 Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 573 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libx11-6, libxext6, libxi6, libxmu6 Conflicts: libglew1 Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libs Filename: pool/main/g/glew/libglewmx1.9_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 164558 SHA256: 512798738909e8061ec2ac8ed282074f2ff2eef5d37fcd34bb8a479eb006e326 SHA1: f670d8f8e974dd751c91476af5ad07e1e72e98bd MD5sum: b2a2202b632dac1665b39deed186d23d Description: OpenGL Extension Wrangler (Multiple Rendering Contexts) For more information about GLEW please refer to the description of the libglewmx-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: libglewmx1.9-dbg Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 122 Depends: neurodebian-popularity-contest, libglewmx1.9 (= 1.9.0-3~bnd1~nd70+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglewmx1.9-dbg_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 33016 SHA256: 1546075ee0ac306518286d26e1b622a641e4e2944ba0949aab231f60bdcb20e1 SHA1: c796caf0a2f82518f7c2e959bd213b5ba595914d MD5sum: 74c5afad0329fed0c7c08649da17fa57 Description: OpenGL Extension Wrangler MX (debugging symbols) 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 (http://www.opengl.org/registry). . This package contains the debugging symbols for libglewmx1.9. Package: libglewmx1.9-dev Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libglew1.9-dev, libglewmx1.9 (= 1.9.0-3~bnd1~nd70+1) Conflicts: libglewmx-dev, libglewmx1.6-dev Provides: libglewmx1.5-dev, libglewmx1.6-dev Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/g/glew/libglewmx1.9-dev_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 8798 SHA256: d8b0d591b7f0cac49ae8bd93c8f5a7ca7849dacae294f05737462dc914d2a015 SHA1: 444dfdfad03400ec28214b4dd4b46e6cd726ae60 MD5sum: 0829a802b32c527758902532e8e24c5e Description: OpenGL Extension Wrangler MX - 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 (http://www.opengl.org/registry). . This package contains the development libraries compiled with GLEW_MX. Package: libguac-client-vnc0 Source: libguac-client-vnc Version: 0.6.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 31 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libcairo2 (>= 1.6.0), libguac3, libvncserver0 Recommends: vnc4server Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: libs Filename: pool/main/libg/libguac-client-vnc/libguac-client-vnc0_0.6.0-1~nd70+1_amd64.deb Size: 11156 SHA256: 468c97f4438d74aba7b5d134b79d858c08b789dd91660f7b500badd715ad78d4 SHA1: cb18f9e8d70649c2d5282340784bf7874cdc3dfc MD5sum: 3a112a584d0db66b188755205d5c3d19 Description: VNC client plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the VNC protocol. Package: libguac-dev Source: libguac Version: 0.6.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 128 Depends: neurodebian-popularity-contest, libguac3 (= 0.6.0-2~nd70+1) Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: libdevel Filename: pool/main/libg/libguac/libguac-dev_0.6.0-2~nd70+1_amd64.deb Size: 29196 SHA256: 9157c44871b1841fcd401f9979aba753b03bde438149ff0782f3e93d14d48c84 SHA1: 1c4736f96ba67b022659705784fc434fb49bcfd0 MD5sum: 8c5b5bb290007f9524f68fc4fa778df1 Description: Development headers for the core Guacamole library The development headers for the core Guacamole library used by guacd and all client plugins. This package is required for development of new client plugins, or for building existing plugins and guacd. Package: libguac3 Source: libguac Version: 0.6.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 44 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libpng12-0 (>= 1.2.13-4) Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: libs Filename: pool/main/libg/libguac/libguac3_0.6.0-2~nd70+1_amd64.deb Size: 18996 SHA256: ff66fd48c04b00670f263d5665f38a48abff7746fdecd4198a7ecf1d41fe5250 SHA1: c15b6001d1f4841b65dddc3073eaff902e2a5ed9 MD5sum: 8234d3926e3b325e0e6bddc639c6079a Description: Core Guacamole library used by guacd and client plugins The core Guacamole library which both guacd and client plugins depend on to provide low-level I/O and protocol support. Package: libinsighttoolkit4-dev Source: insighttoolkit4 Version: 4.2.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25773 Depends: neurodebian-popularity-contest, libinsighttoolkit4.2 (= 4.2.1-2~nd70+1), libgdcm2-dev Recommends: libfftw3-dev, uuid-dev Suggests: insighttoolkit4-examples Conflicts: libinsighttoolkit-dev, libinsighttoolkit3-dev Replaces: libinsighttoolkit-dev Homepage: http://www.itk.org/ Priority: optional Section: libdevel Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4-dev_4.2.1-2~nd70+1_amd64.deb Size: 5275658 SHA256: 00c89e98ade0dc7d3190f0cd1716463bf6aec4760991a8d61117c2ecbbd079a4 SHA1: c73b432714dbc9dc14a40b7939acaea7d50630d9 MD5sum: 9a2b8871b70d67fa8f5fc44a30d9c995 Description: Image processing toolkit for registration and segmentation - development ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the development files needed to build your own ITK applications. Package: libinsighttoolkit4.2 Source: insighttoolkit4 Version: 4.2.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 22235 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgdcm2.2, libhdf5-7, libjpeg8 (>= 8c), libminc2-1, libnetcdfc7, libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), zlib1g (>= 1:1.2.3.3) Homepage: http://www.itk.org/ Priority: optional Section: libs Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4.2_4.2.1-2~nd70+1_amd64.deb Size: 7285294 SHA256: 9bb45e9cad5b6b8689ad5d4f081079321a62d7109dd0c6335ea25db4705136ac SHA1: a1e39e2e995bded3b3f1ecedd5cb18e8ba0f2104 MD5sum: 9632194c6a8271910e8e0daee2833fbe Description: Image processing toolkit for registration and segmentation - runtime ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the libraries needed to run ITK applications. Package: libipmiconsole-dev Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 613 Depends: neurodebian-popularity-contest, libipmiconsole2 (= 1.1.5-3~nd70+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmiconsole-dev_1.1.5-3~nd70+1_amd64.deb Size: 269948 SHA256: bcb89635d1de7bbd1f31ae174f7e36705a1ee147a6cde946d2406427a85c9a8c SHA1: f58e5241a4dcbe735755b3ff4207eadb3d103d64 MD5sum: 49e217917b71fe63c97c7064f0394281 Description: GNU IPMI - ipmiconsole development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libipmiconsole. Package: libipmiconsole2 Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 376 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreeipmi12 (>= 1.1.5), libgcrypt11 (>= 1.4.5) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmiconsole2_1.1.5-3~nd70+1_amd64.deb Size: 237672 SHA256: 66010cb3f1e8832a6f82e9257bc83d53344af75473173983b0f90ffa6736440c SHA1: ea5d4720af83e4a75652a8bd2c80b340d6ede840 MD5sum: 2435cde842f08a4363aae4e2de35faf2 Description: GNU IPMI - Serial-over-Lan library FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . A library for Serial-over-Lan (SOL). Package: libipmidetect-dev Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 234 Depends: neurodebian-popularity-contest, libipmidetect0 (= 1.1.5-3~nd70+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmidetect-dev_1.1.5-3~nd70+1_amd64.deb Size: 176382 SHA256: 00069d14991d47146a60fa798d81431ee5015fe3a578e5804acf5512139f7f74 SHA1: 7375f5b5528efb8432ddd9c674c46ffbc5719cea MD5sum: 6e0b9e201aa48736450124d98306b1f7 Description: GNU IPMI - ipmidetect development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libipmidetect. Package: libipmidetect0 Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 191 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), freeipmi-common (= 1.1.5-3~nd70+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmidetect0_1.1.5-3~nd70+1_amd64.deb Size: 166336 SHA256: 9fce91257f7633d6e092463cd05176393f8bf0bc45afee481b8f4d4485906401 SHA1: a9a9a6870610e626942d2cc092c92573daae5802 MD5sum: f8f66ce78135b6b323c276df4feeee33 Description: GNU IPMI - IPMI node detection library FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . A library for IPMI node detection. Package: libipmimonitoring-dev Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 440 Depends: neurodebian-popularity-contest, libipmimonitoring5 (= 1.1.5-3~nd70+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmimonitoring-dev_1.1.5-3~nd70+1_amd64.deb Size: 226986 SHA256: ee540afb300e3deb17c0f0975984c3317746f7a2db2db2b0015e13b3fa33f76d SHA1: d774591fac16eeb348929e8837d594d6cfb140cf MD5sum: b3fabbac6fac6ed4c75f438fb9742ceb Description: GNU IPMI - ipmimonitoring development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libipmimonitoring. Package: libipmimonitoring5 Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 255 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreeipmi12 (>= 1.1.5), libgcrypt11 (>= 1.4.5) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmimonitoring5_1.1.5-3~nd70+1_amd64.deb Size: 190190 SHA256: 94fabf06a59fccebc2393af51b00b535a22b6911d5dabbde4d807b0f5da9e045 SHA1: 8202e1460e43761586715f8cdb74b57dd7280685 MD5sum: c01c9ad2e377e8280799db669f92bdc5 Description: GNU IPMI - Sensor monitoring library FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . A library for sensor monitoring. Package: libisis-core-dev Source: isis Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libisis-core0 (>= 0.4.7-1~nd70+1), libisis-core0 (<< 0.4.7-1~nd70+1.1~) Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-core-dev_0.4.7-1~nd70+1_all.deb Size: 68948 SHA256: 71ba81e336312edd85331e45ad6c689d1133fe332506a79eb1d4e41946534675 SHA1: 7761d9efa1a6a2cadc67a0f2e546b165f088f855 MD5sum: cc18de68a3f8d8942ad55d38751a2d01 Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides headers and library to develop applications with ISIS. Package: libisis-core0 Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 9875 Depends: neurodebian-popularity-contest, libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.10), libstdc++6 (>= 4.6) Recommends: libisis-ioplugins-common, libisis-ioplugins-dicom Homepage: https://github.com/isis-group Priority: extra Section: libs Filename: pool/main/i/isis/libisis-core0_0.4.7-1~nd70+1_amd64.deb Size: 2019594 SHA256: cdcafe66dd15c461a2729edeb8c576d48e2c7851ef89cfab489fd1ddfced9393 SHA1: b1f67caecec61424ed12e4ec992f7d1c03bb97f1 MD5sum: 43729e2dbee8c7de1dba835c87c60324 Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This Package provides the core library needed by all applications that are build upon ISIS. Package: libisis-ioplugins-common Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5602 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-iostreams1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libbz2-1.0, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.1), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libvia2, zlib1g (>= 1:1.1.4) Homepage: https://github.com/isis-group Priority: extra Section: libs Filename: pool/main/i/isis/libisis-ioplugins-common_0.4.7-1~nd70+1_amd64.deb Size: 1459852 SHA256: 8c2acccca750c8f3eaf4fe0d731df4a4fdd413adfd3f1474f6dec1daba0f5de4 SHA1: b2ee68c5d832ecc73abc72e937c703518f23ee4d MD5sum: 4951b4b7f40ae7256669a41dedde5026 Description: data format plugins for the ISIS framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides plugins for data in NIfTI, PNG, VISTA format, raw-data access, as well as plugins for gzip-compression and tar-archive support. Package: libisis-ioplugins-dicom Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1414 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.1), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libtiff4, zlib1g (>= 1:1.1.4) Homepage: https://github.com/isis-group Priority: extra Section: science Filename: pool/main/i/isis/libisis-ioplugins-dicom_0.4.7-1~nd70+1_amd64.deb Size: 371354 SHA256: bf0e86c60f5afb37972c9288c23f8bce8d840764fa8dac36955751a4a6516128 SHA1: 45780c7dc372670244e5190d46bf8bfcc9c5161f MD5sum: 7a94ddb3a274e1d32dfe1ca95fe9d563 Description: dicom io plugin for the ISIS framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides a plugin to read data from dicom datasets. It reads single files, or whole directories (a DICOMDIR is not needed). Package: libisis-qt4-0 Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 162 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.1), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6) Conflicts: isis-qt4 Replaces: isis-qt4 Homepage: https://github.com/isis-group Priority: extra Section: libs Filename: pool/main/i/isis/libisis-qt4-0_0.4.7-1~nd70+1_amd64.deb Size: 48828 SHA256: c67b545cc531d7bda63c31b639d8e4d6489de01d0372cf8dfd45d22f31920933 SHA1: 6888db247f5fd474e1fb39f24f81c8c1e6c94ef1 MD5sum: c12584164ff4e2cf63435f53322009a3 Description: Qt4 bindings for ISIS data I/O framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libisis-qt4-dev Source: isis Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Depends: neurodebian-popularity-contest, libisis-qt4-0 (>= 0.4.7-1~nd70+1), libisis-qt4-0 (<< 0.4.7-1~nd70+1.1~), libqt4-dev Conflicts: isis-qt4-dev Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-qt4-dev_0.4.7-1~nd70+1_all.deb Size: 5992 SHA256: f848c976204b1b3090c9bcba159204365ee5620986f0cadd15bc6a6b8a9dde80 SHA1: a9cc9f1a3bd89a7545ffe60b6ccc874c874986a6 MD5sum: 96ef7f5956383a9fe46cea8c8843d7cd Description: Qt4 bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libmcpanel-dev Source: mcpanel Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd70+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libdevel Filename: pool/main/m/mcpanel/libmcpanel-dev_0.0-1~nd70+1_amd64.deb Size: 2392 SHA256: ae18b5e17e9b254156d3ffb73fdc17c8abe7e9da8d32a5408930a6c7dc7037a9 SHA1: 352b39d8b8b6a630b1dea50de1ec6d89bf41eb07 MD5sum: 30b0c24b2f9a2c1ac9b8557a5ab47712 Description: Library to display multichannel data in realtime (Developement files) This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. . This package contains the files needed to compile and link programs which use mcpanel Package: libmcpanel0 Source: mcpanel Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 145 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.14.0), libgtk2.0-0 (>= 2.14.0), libpango1.0-0 (>= 1.14.0), librtfilter1 (>= 1.0) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libs Filename: pool/main/m/mcpanel/libmcpanel0_0.0-1~nd70+1_amd64.deb Size: 54376 SHA256: 25aa09e49b93c8e306d3cf6fe7a3e2fa376cb1a39304b1944b3129b7eee5f14d SHA1: 9bec70ba1a28eb6562c376f097085683f527fd28 MD5sum: 5cee97a95228cbc6b03adf60abe5be4f Description: Library to display multichannel data in realtime This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. Package: libmcpanel0-dbg Source: mcpanel Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 333 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd70+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: debug Filename: pool/main/m/mcpanel/libmcpanel0-dbg_0.0-1~nd70+1_amd64.deb Size: 122690 SHA256: 76d45f284f3fd61530546a5cb9202c5043d150312bfc6542246b5850a9bedd03 SHA1: 7ddee15e9c74c06b3dcee67f03af9c4801a491e7 MD5sum: 1a04d1cab0226f858266d1b58d10baae Description: Library to display multichannel data in realtime (Debugging symbols) This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. . This package contains the debugging information of the library. Package: libmtcp-dev Source: dmtcp Version: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libmtcp1 (= 1.2.5-1~nd70+1) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/d/dmtcp/libmtcp-dev_1.2.5-1~nd70+1_amd64.deb Size: 5570 SHA256: 3e6a6093b56abd7f811c905b83c99d29c9ddff9b1e9c3d1a241f98cdf2f4d6e0 SHA1: 2608f9ee849370a6fc1e296c25c6a342721d39db MD5sum: d38ba8c402370b532a21ce89496b2add Description: Developer package for libmtcp 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-Window applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides header files needed for building programs with libmtcp. Package: libmtcp1 Source: dmtcp Version: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 110 Depends: neurodebian-popularity-contest, libc6 (>= 2.4) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libmtcp1_1.2.5-1~nd70+1_amd64.deb Size: 45304 SHA256: cd44cdbb087c1c2b1461cbbfb63427d76b73d8b8dd270bbbb25ff55d95380e5a SHA1: 2f6323c122533988a850a78cc9e09495516dbc76 MD5sum: 64abe111633f6316aa32de5f03019d2c Description: DMTCP library needed for checkpointing a standalone process 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-Window applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides libmtcp which is needed by DMTCP to checkpoint a single standalone process. Package: libodin-dev Source: odin Version: 1.8.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21615 Depends: neurodebian-popularity-contest Homepage: http://od1n.sourceforge.net Priority: extra Section: libdevel Filename: pool/main/o/odin/libodin-dev_1.8.5-1~nd70+1_amd64.deb Size: 4361668 SHA256: 4d5085d34b39e29e9110032375320b0b543ba461f9389db6d472740b955ebfd5 SHA1: b9fc7fc8299aa37969777a4c43479e25e1d6a521 MD5sum: 73fe355f921d5ecd3f9a27590cd9d633 Description: static libraries and header for ODIN sequences This package provides static libraries and headers of the ODIN libraries odindata, adinpara, odinqt, odinseq and tjutils. They are required for building magnetic resonance imaging (MRI) sequences with ODIN. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 197 Depends: neurodebian-popularity-contest Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-4~nd70+1_amd64.deb Size: 42574 SHA256: edb2c179742df10e47c5529cd89af0948271294629a0a4763ae834bf0c18ed50 SHA1: 0cc9c3de65126a1ecf2b43174a5dd9aa98d07f61 MD5sum: 1faf21b27a88956f37f2dca7a422a096 Description: openmeeg library -- development files OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides static libraries and header files. Package: libopenmeeg1 Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1779 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmatio0, libstdc++6 (>= 4.6) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/libopenmeeg1_2.0.0.dfsg-4~nd70+1_amd64.deb Size: 266080 SHA256: 8a691f73fca1e801ed17863649a177dd9f481ffe8dbb6534de298cd71ae925e4 SHA1: fb4d437654dd0c7e4f275af9de3d247c96092dae MD5sum: e6b4db0cbaa8df1103508dd63bab2fdf Description: library for solving EEG and MEG forward and inverse problems OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides dynamic libraries. Package: libopenwalnut1 Source: openwalnut Version: 1.3.1+hg5849-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6698 Depends: neurodebian-popularity-contest, ttf-liberation (>= 1.0.0), libboost-date-time1.49.0 (>= 1.49.0-1), libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-signals1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libopenscenegraph80, libstdc++6 (>= 4.6) Suggests: nvidia-glx | fglrx-glx Homepage: http://www.openwalnut.org Priority: extra Section: libs Filename: pool/main/o/openwalnut/libopenwalnut1_1.3.1+hg5849-1~nd70+1_amd64.deb Size: 1838914 SHA256: ef5874c1a2c01f1e2e9d8c995c1e7c60107f3105f3dd8f58da6c798eb5a99021 SHA1: 591bdadc6ca99bd45a8f03e71d11bf71b7fbb946 MD5sum: 0ab4b9d7573d1a6874b97b47caa08278 Description: Framework for multi-modal medical and brain data visualization 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: 1.3.1+hg5849-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1797 Depends: neurodebian-popularity-contest, libopenwalnut1 (= 1.3.1+hg5849-1~nd70+1), libgl1-mesa-dev | libgl-dev, libopenscenegraph-dev (>= 3.0.0), libopenthreads-dev (>= 3.0.0), libboost-dev (>= 1.46.0), libboost-program-options-dev (>= 1.46.0), libboost-thread-dev (>= 1.46.0), libboost-filesystem-dev (>= 1.46.0), libboost-date-time-dev (>= 1.46.0), libboost-system-dev (>= 1.46.0), libboost-signals-dev (>= 1.46.0), libboost-regex-dev (>= 1.46.0), libeigen3-dev (>= 3.0.0) Homepage: http://www.openwalnut.org Priority: extra Section: libdevel Filename: pool/main/o/openwalnut/libopenwalnut1-dev_1.3.1+hg5849-1~nd70+1_amd64.deb Size: 304160 SHA256: a5f42029713abcaf65959d78d087ac3d7a991c32ffb7b16bae6f99d7f676e615 SHA1: 04253f9713336520f2cea39c5c4d7eaaefb26a3e MD5sum: 7190efff179527dff515c502d09ceaee Description: Development files for the OpenWalnut visualization framework 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: 1.3.1+hg5849-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39512 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.3.1+hg5849-1~nd70+1_all.deb Size: 4548600 SHA256: 90203b0a3ac595cb4a23609f394cbff8151a65200e80d213efe2dd0d5aa857a6 SHA1: 0d07fa4dbbb13da94af06d1415a4f1de03cf0296 MD5sum: 5491afc50fca47708ff44779e9d56238 Description: Developer documentation for the OpenWalnut visualization framework OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: libpam-cgroup Source: libcgroup Version: 0.37.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libcgroup1, libpam0g (>= 0.99.7.1) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: admin Filename: pool/main/libc/libcgroup/libpam-cgroup_0.37.1-1~nd70+1_amd64.deb Size: 7792 SHA256: fd0540e8e09f8ec8b4f042032b107a65f506c52a9ec0e982c92be0f9affd3b66 SHA1: 6bf5ae5b92a7138626e6484e649f6d8357435f90 MD5sum: 2d86a22ab20cb9cb160e320a13e0daf9 Description: PAM module to move a user session into a cgroup Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . This PAM module will move a user session into an existing cgroup by attempting to match uid and gid against the defined cgroup rules configuration. Package: librtfilter-dev Source: rtfilter Version: 1.1-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, librtfilter1 (= 1.1-4~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/rtfilter.html Priority: extra Section: libdevel Filename: pool/main/r/rtfilter/librtfilter-dev_1.1-4~nd70+1_amd64.deb Size: 12600 SHA256: a12d14b8b6725556fe36db24d90cb0ca61525ad21dd14c0c5235e575b478d849 SHA1: 1461872afc86c1462cbe64684cffb3dd8d7e6813 MD5sum: bcc5f3b28fbffcc5450b5fc0cd43d365 Description: realtime digital filtering library (development files) rtfilter is a library that provides a set of routines implementing realtime digital filter for multichannel signals (i.e. filtering multiple signals with the same filter parameters). It implements FIR, IIR filters and downsampler for float and double data type (both for real and complex valued signal). Additional functions are also provided to design few usual filters: Butterworth, Chebyshev, windowed sinc, analytical filter... . One of the main differences from other libraries providing digital signal processing is that the filter functions have been specifically designed and optimized for multichannel signals (from few channels to several hundred). . This package contains the files needed to compile and link programs which use rtfilter. Package: librtfilter1 Source: rtfilter Version: 1.1-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 56 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/rtfilter.html Priority: extra Section: libs Filename: pool/main/r/rtfilter/librtfilter1_1.1-4~nd70+1_amd64.deb Size: 31184 SHA256: 5cb21d5d22780c438cb371f0287a238a330dd2cf54591b6559ae2f8b3213a192 SHA1: 9aa1d3347cd23c923b78a5b95354f0ff4b8dc6bc MD5sum: 6909e1084963448af461cd757243a098 Description: realtime digital filtering library rtfilter is a library that provides a set of routines implementing realtime digital filter for multichannel signals (i.e. filtering multiple signals with the same filter parameters). It implements FIR, IIR filters and downsampler for float and double data type (both for real and complex valued signal). Additional functions are also provided to design few usual filters: Butterworth, Chebyshev, windowed sinc, analytical filter... . One of the main differences from other libraries providing digital signal processing is that the filter functions have been specifically designed and optimized for multichannel signals (from few channels to several hundred). Package: librtfilter1-dbg Source: rtfilter Version: 1.1-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 41 Depends: neurodebian-popularity-contest, librtfilter1 (= 1.1-4~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/rtfilter.html Priority: extra Section: debug Filename: pool/main/r/rtfilter/librtfilter1-dbg_1.1-4~nd70+1_amd64.deb Size: 36504 SHA256: e734a37b8c13f0b21fde39054b56bbbe3e12d46a3e4038b5cbcf268b8abdac4b SHA1: f571aa39a5b5bb7258f3827e4c4de0cd4a1b7032 MD5sum: ce9f939e354e6927194126c8e97c56ce Description: realtime digital filtering library (debugging symbols) rtfilter is a library that provides a set of routines implementing realtime digital filter for multichannel signals (i.e. filtering multiple signals with the same filter parameters). It implements FIR, IIR filters and downsampler for float and double data type (both for real and complex valued signal). Additional functions are also provided to design few usual filters: Butterworth, Chebyshev, windowed sinc, analytical filter... . One of the main differences from other libraries providing digital signal processing is that the filter functions have been specifically designed and optimized for multichannel signals (from few channels to several hundred). . This package provides the debugging symbols of the library. Package: libshogun-dev Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16571 Depends: neurodebian-popularity-contest, libshogun11 (= 1.1.0-6~nd70+1) Homepage: http://www.shogun-toolbox.org Priority: optional Section: libdevel Filename: pool/main/s/shogun/libshogun-dev_1.1.0-6~nd70+1_amd64.deb Size: 2758284 SHA256: a7278b487e1a7f8be2c14b93575e4af06c59e48e171d6b39af8bb3dfc47d7fb0 SHA1: ca0eef589807b01c0399b4fde7bd38711f1ab89a MD5sum: 198b464ae98962a69da45ffe9918e6d6 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables. Package: libshogun11 Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5456 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.2), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20120614), liblzo2-2, libstdc++6 (>= 4.6), libxml2 (>= 2.7.4), zlib1g (>= 1:1.1.4) Conflicts: libshogunui0, libshogunui1, libshogunui2, libshogunui3, libshogunui4, libshogunui5, libshogunui6 Homepage: http://www.shogun-toolbox.org Priority: optional Section: libs Filename: pool/main/s/shogun/libshogun11_1.1.0-6~nd70+1_amd64.deb Size: 1579418 SHA256: e312c308fedd2feade89316fa5221d7adf3370374bf5de523a988cd2b4b8b727 SHA1: 61bf9084866c2334fb40db46adaa023721d58e36 MD5sum: 2a22419cd27daa66f15efc778e600cd9 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the core library with the machine learning methods and ui helpers all interfaces are based on. Package: libvia-dev Source: via Version: 2.0.4-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 969 Depends: neurodebian-popularity-contest, libvia2 (= 2.0.4-2~nd70+1), x11proto-core-dev Conflicts: via-dev Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: libdevel Filename: pool/main/v/via/libvia-dev_2.0.4-2~nd70+1_amd64.deb Size: 212220 SHA256: 44b0d7f45533514a58d18da7477f8147320d23d1ca2e2da513ca0638c84372f0 SHA1: 279054de6615f664af82030520218d6bfd1d2d22 MD5sum: be7834dfbe616b25919944ea6968072e Description: library for volumetric image analysis VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the header files and static libraries of vialib, vxlib and viaio. Package: libvia-doc Source: via Version: 2.0.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 903 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd70+1_all.deb Size: 118466 SHA256: c508ad5f2de2d726a6ec321a5dda11ae53d8d1991ad9d407c85cfd9190a25184 SHA1: 20c0141728ccf9539a2a460c758d63970ddd85a2 MD5sum: 7094bbe0e4041f7c7ad8b07781132693 Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: libvia2 Source: via Version: 2.0.4-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 568 Depends: neurodebian-popularity-contest, lesstif2 (>= 1:0.94.4), libatlas3gf-base, libc6 (>= 2.7), libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libsm6, libx11-6, libxext6, libxmu6, libxt6 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: libs Filename: pool/main/v/via/libvia2_2.0.4-2~nd70+1_amd64.deb Size: 172346 SHA256: 9006b22186b7f0e8cbc4e3914bd7bcb674379c3b8eaa01b69066eae7749a8415 SHA1: 69879a36111156fa87c5ff68e17d904d9487bd71 MD5sum: d2fc837ccadee5f7581304f29ad60a77 Description: library for volumetric image analysis VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package contains the shared libraries of vialib, vxlib and viaio. Package: libvrpn-dev Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 702 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd70+1), libvrpnserver0 (= 07.30+dfsg-1~nd70+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libdevel Filename: pool/main/v/vrpn/libvrpn-dev_07.30+dfsg-1~nd70+1_amd64.deb Size: 218312 SHA256: e2c64f9ab32628f52da98f7e959436435b8eeb050e6d7aba718c2f1fdf9ea038 SHA1: e212814878ef7949899e99aa05e51b5c93fe0b46 MD5sum: 4ba1b79a9a3682478198d778ea0ef38e Description: Virtual Reality Peripheral Network (development files) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the development files Package: libvrpn0 Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 585 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpn0_07.30+dfsg-1~nd70+1_amd64.deb Size: 232324 SHA256: 207a26dc84e865944ad5ede80d98f46629f66d1a2f4d2d5aa69fb8609fcaed49 SHA1: 262e5f1720d08bdf3bdb4ed7c4b3370e8a2c4d50 MD5sum: 7069f631935755d5e71f4e2693aee38c Description: Virtual Reality Peripheral Network (client library) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the client shared library Package: libvrpnserver0 Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1422 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpnserver0_07.30+dfsg-1~nd70+1_amd64.deb Size: 492400 SHA256: 158b3511b781eaf2b92acbff2451b966dea1824c104776a34027bedcd5c5ea3a SHA1: 098fa7e6edc42190c8170ccb7fa3c5e07d06fb7d MD5sum: a679028df66cf8f8fcd5be66573d0696 Description: Virtual Reality Peripheral Network (server library) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the shared library use in the VRPN server Package: libvtk-java Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10911 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libmysqlclient16 (>= 5.1.50-1), libpq5, libstdc++6 (>= 4.1.1), libvtk5.8, zlib1g (>= 1:1.1.4) Suggests: libvtk5-dev (= 5.8.0-7+b0~nd70+1), vtk-examples, vtk-doc, java-virtual-machine Homepage: http://www.vtk.org/ Priority: optional Section: java Filename: pool/main/v/vtk/libvtk-java_5.8.0-7+b0~nd70+1_amd64.deb Size: 4911660 SHA256: 68229a9bf87e3a1b9799f6ca833ed4bc50e6bff78f0b5459f2d5f37adaa965d4 SHA1: 63c1b0a65daaa2df22b45079c1a88bca460e4b78 MD5sum: d8c8e0aa2f5e0c6e630eca91a5dec181 Description: Visualization Toolkit - A high level 3D visualization library - java The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK Java language support. Package: libvtk5-dev Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12852 Depends: neurodebian-popularity-contest, libvtk5.8 (= 5.8.0-7+b0~nd70+1), libgl1-mesa-dev | libgl-dev, libglu1-mesa-dev | libglu-dev, libx11-dev, libxt-dev, x11proto-core-dev, libc6-dev, libxss-dev, libxft-dev, libexpat-dev, libjpeg-dev, libpng-dev, libtiff-dev, zlib1g-dev, tcl8.5-dev, tk8.5-dev, libavformat-dev, libavutil-dev, libavcodec-dev, libswscale-dev, libgl2ps-dev, libfreetype6-dev, libxml2-dev, libpq-dev, libnetcdf-dev, libmysqlclient-dev, mpi-default-dev, libqt4-dev Suggests: vtk-examples, vtk-doc Conflicts: libvtk-dev, libvtk32-dev, libvtk4-dev Replaces: libvtk-dev, libvtk32-dev, libvtk4-dev Homepage: http://www.vtk.org/ Priority: optional Section: libdevel Filename: pool/main/v/vtk/libvtk5-dev_5.8.0-7+b0~nd70+1_amd64.deb Size: 2565214 SHA256: 13c1ba8002c22cd09b24bbff4334db11310c4fe26eb8ce46c7c85eb1ae90bdf8 SHA1: 83441de1304fc5cb4136fd128b1f601274a215eb MD5sum: 2581309d3c9ac507f18bed2d8075bd29 Description: VTK header files for building C++ code The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK header files required to compile C++ programs that use VTK to do 3D visualisation. Package: libvtk5-qt4-dev Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 549 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.1.1), libvtk5.8-qt4 (= 5.8.0-7+b0~nd70+1), libvtk5-dev (= 5.8.0-7+b0~nd70+1) Conflicts: libvtk5-qt3-dev Breaks: libvtk5-qt4 (<< 5.4.2-8) Replaces: libvtk5-qt4 (<< 5.4.2-8) Homepage: http://www.vtk.org/ Priority: optional Section: libdevel Filename: pool/main/v/vtk/libvtk5-qt4-dev_5.8.0-7+b0~nd70+1_amd64.deb Size: 109450 SHA256: f8a5d96cd72c4689d152150c988da0788fbf7a5f8f03642744d33b6a51b97cf5 SHA1: d4274d37e0fda0b58d5f54600cd1a248d2f8a7b6 MD5sum: 2918cf3fce1921511fbcac4acc8173f9 Description: Visualization Toolkit - A high level 3D visualization library - Qt devel The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK header files required to compile C++ programs that use VTK GUI support for Qt4. Package: libvtk5.8 Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 47808 Depends: neurodebian-popularity-contest, libavcodec53 (>= 4:0.8-1~) | libavcodec-extra-53 (>= 4:0.8-1~), libavformat53 (>= 4:0.8-1~) | libavformat-extra-53 (>= 4:0.8-1~), libavutil51 (>= 4:0.8-1~) | libavutil-extra-51 (>= 4:0.8-1~), libc6 (>= 2.7), libexpat1 (>= 1.95.8), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libgl2ps0, libjpeg8 (>= 8c), libmysqlclient16 (>= 5.1.50-1), libnetcdfc++5, libnetcdfc6, libopenmpi1.3, libpng12-0 (>= 1.2.13-4), libpq5, libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libsqlite3-0 (>= 3.5.9), libstdc++6 (>= 4.6), libswscale2 (>= 4:0.8-1~) | libswscale-extra-2 (>= 4:0.8-1~), libtiff4 (>= 3.9.5-2), libx11-6, libxml2 (>= 2.7.4), libxt6, zlib1g (>= 1:1.2.3.3) Suggests: openmpi-bin | lam-runtime, libvtk5-dev, vtk-examples, vtk-doc Conflicts: libvtk, libvtk32, libvtk4, libvtk4c2, libvtk4c2a, libvtk5, python-vtk (<< 4.4) Replaces: libvtk, libvtk32, libvtk4, libvtk4c2, libvtk4c2a, libvtk5 Homepage: http://www.vtk.org/ Priority: optional Section: libs Filename: pool/main/v/vtk/libvtk5.8_5.8.0-7+b0~nd70+1_amd64.deb Size: 15257918 SHA256: ed0323761444227f6664e788787b82249dc87fe12f4b55d6785bdc8fb259e472 SHA1: 71433fdf990dc407df7b0f4acdbcf6eeb3263897 MD5sum: 67a046cfe213ed96361d228646b5cdd0 Description: Visualization Toolkit - A high level 3D visualization library - runtime The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . VTK enables users to concentrate on their work by providing a large number of excellent and feature packed high level functions that do visualization. The library needs OpenGL to render the graphics and for Linux machines Mesa is necessary. The terms/copyright can be read in /usr/share/doc/vtk/README and README.html. VTK-Linux-HOWTO has information about using vtk, getting documentataion or help and instructions on building VTK. . This package provides the shared libraries needed to run C++ programs that use VTK. . To compile C++ code that uses VTK you have to install libvtk5-dev. Package: libvtk5.8-qt4 Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1360 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libqt4-network (>= 4:4.5.3), libqt4-sql (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.4.0), libvtk5.8 Homepage: http://www.vtk.org/ Priority: optional Section: libs Filename: pool/main/v/vtk/libvtk5.8-qt4_5.8.0-7+b0~nd70+1_amd64.deb Size: 484074 SHA256: 422d8109b5f7b7b0af999c02a9568d0e03070c42e4202466d109bc76cee92c8a SHA1: 9d54d7d16173da33685bbb978b2bc229d343b656 MD5sum: 282bad631a4e495636f5ad980f6ca789 Description: Visualization Toolkit - A high level 3D visualization library - Qt runtime The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK GUI support for Qt4. Package: libvw-dev Source: vowpal-wabbit Version: 7.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2528 Depends: neurodebian-popularity-contest, libvw0 (= 7.2-1~nd70+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_7.2-1~nd70+1_amd64.deb Size: 573606 SHA256: 1bd83b12320985fa94938203160324aab284ed7b510df7863067817d7375a4f0 SHA1: 876c0def27fdf60951512d29cc08aea749723bb4 MD5sum: 2d74897fe90efa5f8f52963d8e4e39e5 Description: fast and scalable online machine learning algorithm - development files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains development files needed to compile and link programs which use vowpal-wabbit's libraries. Package: libvw0 Source: vowpal-wabbit Version: 7.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 747 Depends: neurodebian-popularity-contest, libboost-program-options1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.2.3.3) Homepage: http://hunch.net/~vw/ Priority: optional Section: libs Filename: pool/main/v/vowpal-wabbit/libvw0_7.2-1~nd70+1_amd64.deb Size: 312368 SHA256: 492da9246f0ad55cc49ff29d1ad27edd046688fc458c1cb172f56758bb2fbfac SHA1: 5ad3c42dee3f00ca6bd1c213eb945cd229fb713b MD5sum: 7753d07d3e29d1012593eb21ccb432d5 Description: fast and scalable online machine learning algorithm - dynamic library Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains vowpal-wabbit's dynamic libraries. Package: libxdffileio-dev Source: xdffileio Version: 0.3-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, libxdffileio0 (= 0.3-1~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/xdffileio.html Priority: extra Section: libdevel Filename: pool/main/x/xdffileio/libxdffileio-dev_0.3-1~nd70+1_amd64.deb Size: 27776 SHA256: 12fa076aecf2cefc865ef21c27bfcd3701f6dcf432c82a8cc68507a08e3b73f6 SHA1: 9f6c4dd3b896bb19869a371c2c09adbcf1dad4bf MD5sum: f40cbfdc1f04fb44b430421e0af72b12 Description: Library to read/write EEG data file formats (development files) xdffileio is a library that provides a unified interface for writing and reading various biosignal file formats in realtime (i.e. streaming). It has been designed to provide a flexible, consistent and generic interface to all supported file formats while minimizing the overhead the function calls: the heaviest operations (type casting, scaling and formatting) are offloaded into a separated thread. This design makes its particularly suitable to be directly used in a data acquisition loop (like in electrophysiology recording or in Brain-Computer Interfaces (BCI)). . The genericity of the interface makes trivial various operations like transformation of a recorded file or its conversion to another file format. xdffileio currently supports EDF, BDF, GDF1 and GDF2 file formats and more will be added in future. . This package contains the files needed to compile and link programs which use xdffileio. Package: libxdffileio0 Source: xdffileio Version: 0.3-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 79 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/xdffileio.html Priority: extra Section: libs Filename: pool/main/x/xdffileio/libxdffileio0_0.3-1~nd70+1_amd64.deb Size: 42544 SHA256: bfc34faa9f7af89d20e60b8e371a2e1c6a7ee0a5725f232141f7d65d0cc1a553 SHA1: 27bac77b653abc13c477dff836950cca4f152fe8 MD5sum: 9f08c8270d74247e9d090ca341ee2adc Description: Library to read/write EEG data file formats xdffileio is a library that provides a unified interface for writing and reading various biosignal file formats in realtime (i.e. streaming). It has been designed to provide a flexible, consistent and generic interface to all supported file formats while minimizing the overhead of the function calls: the heaviest operations (type casting, scaling and formatting) are offloaded into a separated thread. This design makes its particularly suitable to be directly used in a data acquisition loop (like in electrophysiology recording or in Brain-Computer Interfaces (BCI)). . The genericity of the interface makes trivial various operations like transformation of a recorded file or its conversion to another file format. xdffileio currently supports EDF, BDF, GDF1 and GDF2 file formats and more will be added in future. Package: libxdffileio0-dbg Source: xdffileio Version: 0.3-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 71 Depends: neurodebian-popularity-contest, libxdffileio0 (= 0.3-1~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/xdffileio.html Priority: extra Section: debug Filename: pool/main/x/xdffileio/libxdffileio0-dbg_0.3-1~nd70+1_amd64.deb Size: 61922 SHA256: 9012007e5d7c465db911773f7fb8b4e465c884576555f0132ea94a3bdbcd5e25 SHA1: 4b9f9ff0afd69fd137d51301d1cebd5b74c3c443 MD5sum: 3d5eb6cd54918ea7762bed10926da8d2 Description: Library to read/write EEG data file formats (debugging symbols) xdffileio is a library that provides a unified interface for writing and reading various biosignal file formats in realtime (i.e. streaming). It has been designed to provide a flexible, consistent and generic interface to all supported file formats while minimizing the overhead the function calls: the heaviest operations (type casting, scaling and formatting) are offloaded into a separated thread. This design makes its particularly suitable to be directly used in a data acquisition loop (like in electrophysiology recording or in Brain-Computer Interfaces (BCI)). . The genericity of the interface makes trivial various operations like transformation of a recorded file or its conversion to another file format. xdffileio currently supports EDF, BDF, GDF1 and GDF2 file formats and more will be added in future. . This package provides the debugging symbols of the library. Package: matlab-support-dev Source: matlab-support Version: 0.0.19~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 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_0.0.19~nd70+1_all.deb Size: 7222 SHA256: c2f02411c154a5e7ee649826684229a6039ca11e4202895d685b17c583367a0f SHA1: bf2bb4f466a020900a5b75d882a5daaacc6cc8ce MD5sum: 27847c10f995a1766934bf85ef8602ac 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: mitools Source: odin Version: 1.8.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7651 Depends: neurodebian-popularity-contest, libatlas3gf-base, libblitz0ldbl, libc6 (>= 2.3.2), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libnifti2, libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.6), libvtk5.8, zlib1g (>= 1:1.1.4), dcmtk Recommends: grace Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/mitools_1.8.5-1~nd70+1_amd64.deb Size: 2682030 SHA256: 93d1b2bfe366ac52de8dea113da8defee7917c123dc55b27128564c7da0a55bc SHA1: 3d5e294467659d93b09bcaa62d479863e1eebe71 MD5sum: 91f871b37ad9f70a8bfd938c073f3a46 Description: view, convert and perform basic maths with medical image datasets The three contained tools micalc, miconv and miview are handy command-line utilities for converting, manipulating and viewing medical image data in various formats (DICOM, NIfTI, PNG, binary data, ...). Package: mriconvert Version: 2.0.250-1+nd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2825 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.12.1), libwxgtk2.8-0 (>= 2.8.12.1) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.250-1+nd1~nd70+1_amd64.deb Size: 916872 SHA256: cb6fb47db7c34d1a9feb8f1823d6f6f474eda33a22c368cb2b45a05768b199bf SHA1: 01e0ec1c200b91fcd69caef637da25877560aa87 MD5sum: e369f0c706fe20ba376e72613bd1f76e 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: 0.20120505.1~dfsg.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21364 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), 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_0.20120505.1~dfsg.1-1~nd70+1_amd64.deb Size: 6945810 SHA256: 771d330ecb418c07638005c30213e9830a4259d2018c551e8c5c21d705abc893 SHA1: b6b444e533668c285b22070ba4a44496efae1627 MD5sum: f99ffeb03e44466506bfe7f2ec816ada 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: 0.20120505.1~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1681 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd70+1_all.deb Size: 1666896 SHA256: 452a32a313173f9280306a97d3c4cbebc31e488a966f740955d2a828ad63a839 SHA1: 8d3c10732e9f7eccedb844c369010c4cfae76feb MD5sum: aa879d59797cd6348b69c43277fa61a4 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: 0.20120505.1~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 982 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd70+1_all.deb Size: 738386 SHA256: 05857a774647cba8155dcf527dc5f256cdccf239b2e82675af5250acfebd1359 SHA1: 07b2f3b7ebdc74e4a91d8caa61ab08d2a1cd3807 MD5sum: fa0eb7fa2927fd6577e0a6ae97e02ec8 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: 0.2.10-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8119 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libatkmm-1.6-1 (>= 2.22.1), libc6 (>= 2.2.5), 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 (>= 2.22.0), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.30.0), libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1c2a (>= 1:2.24.0), libice6 (>= 1:1.0.0), libpango1.0-0 (>= 1.14.0), libpangomm-1.4-1 (>= 2.27.1), libsigc++-2.0-0c2a (>= 2.0.2), libsm6, libstdc++6 (>= 4.6), 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_0.2.10-1~nd70+1_amd64.deb Size: 2610938 SHA256: 4c9909e37837eb715ab16f3799ff8404b1777fd736aa4fb7164493c9f587a39a SHA1: c321d56c1e3766668ac1936e9aa34ff0d92747fb MD5sum: 4f9bdb1f95097e2237b12ef9ac18c38c 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: 0.2.10-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3491 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.10-1~nd70+1_all.deb Size: 3322016 SHA256: c2268744669b0b2143112d1a1461fb4a5b99e9c102a4852e65f0d83a711ae63a SHA1: 3cdd127233e21960d2567367934029df0a69d2ec MD5sum: 8ff9467f45ceac4eb7425623f6545507 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: mwrap Version: 0.33-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 275 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Recommends: octave Homepage: http://www.cims.nyu.edu/~dbindel/mwrap/ Priority: extra Section: devel Filename: pool/main/m/mwrap/mwrap_0.33-1~nd70+1_amd64.deb Size: 218988 SHA256: c715af214c6837df83c0b9eb2558f57a8992e3c11a658e53836baaae9b34296c SHA1: 44f72ae8fa0c5bfbccc01f24fe5d3085415689b2 MD5sum: 3ed7bbc623b0fe797f1f990df999a281 Description: Octave/MATLAB mex generator MWrap is an interface generation system in the spirit of SWIG or matwrap. From a set of augmented Octave/MATLAB script files, MWrap will generate a MEX gateway to desired C/C++ function calls and Octave/MATLAB function files to access that gateway. The details of converting to and from Octave/MATLAB's data structures, and of allocating and freeing temporary storage, are hidden from the user. Package: neurodebian-desktop Source: neurodebian Version: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 142 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_0.31~nd70+1_all.deb Size: 115314 SHA256: b9170c9731932c00415af37d1751069de4f1398eb9116f072cbd46501a0666a2 SHA1: 0cc0094d3964d8ba2cf3cf5a6c9b596b3cbbfb4e MD5sum: ee8e230a143dc4e63b804a0fec575dd9 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: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5762 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.31~nd70+1_all.deb Size: 5351202 SHA256: cad43d5482d3da721bb836d7630fc36ac35b02e51990ad80fe176babdca00574 SHA1: a90cc8a7bac8c790ae943288ee7e27dd4f4aa7a0 MD5sum: 59a4723a7715ad5d742668a9e4cc2bb7 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: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.31~nd70+1_all.deb Size: 15192 SHA256: d7957826c7b08285bdef6d5ba43a68bb594fad9fbc71807fbf492a4338efcc38 SHA1: bc6aae33a26c40297523ae2da57fa2fe13327420 MD5sum: e9a936b8d260998e7c3f28813f4d6076 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: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.31~nd70+1_all.deb Size: 7486 SHA256: d7277f549050c006cd29e5ed2abe115b02cc45e9ac1432a123a5a00f96476249 SHA1: 56e60f56cd424d1f25df3e14eaf37fad4c844e4b MD5sum: 7db731e36f5df6e96a98b41e39097726 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: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.31~nd70+1_all.deb Size: 6704 SHA256: 3a0d218aa948766a64bcbb5f891a3cd1ff6c459aa1e346322f90fe7e79e2b6c5 SHA1: e5acd691f9ecbac33d1b6535ab8d41be6ebfa7af MD5sum: 9cf4308caf02c86321f48ba1f6f69c40 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: nifti2dicom Version: 0.4.6-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2149 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libgdcm2.2, libinsighttoolkit3.20, libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libtiff4 (>> 3.9.5-3~), zlib1g (>= 1:1.1.4), nifti2dicom-data (= 0.4.6-1~nd70+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom_0.4.6-1~nd70+1_amd64.deb Size: 493204 SHA256: f0fced7015e2dddac569aa3f7e016b2bbab42ea80d55a5bf61f1333d3541caa7 SHA1: cd5a5ab2971893177b77179b2ac59b0f69955476 MD5sum: f102a6e55e7a953c5ed245d9caa56255 Description: convert 3D medical images to DICOM 2D series Nifti2Dicom is a convertion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS. . This package includes the command line tools. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.6-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.6-1~nd70+1_all.deb Size: 615134 SHA256: 83719a424ba0638d067e9e162a8f87bd11de564b1e90421652f15fe7d6bb774c SHA1: 711c9199c005d2a098f95c849f81c6d598abdc84 MD5sum: 90f5a644a728c59535cea93751f104ba Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. 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: nuitka Version: 0.4.4.2+ds-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1762 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.6.6-7~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6) Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.4.4.2+ds-1~nd70+1_all.deb Size: 427640 SHA256: e42575a239778ff9936e789dafb579b0af3b3de20e71e8d6c86530816d5d9c7e SHA1: c82a1f2840c8264ce600e7411bbe3c39da7f9360 MD5sum: 2a73fc7169cbf6f5753f4a43f8d99e4c Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: numdiff Version: 5.6.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 857 Depends: neurodebian-popularity-contest, libc6 (>= 2.3), dpkg (>= 1.15.4) | install-info Homepage: http://nongnu.org/numdiff/ Priority: extra Section: science Filename: pool/main/n/numdiff/numdiff_5.6.0-1~nd70+1_amd64.deb Size: 597436 SHA256: 9a87c4bc5f50898003ea1e5b7d878a07272dd746fb2a09c23f1fe7932e35c076 SHA1: a1273fa841c9e27b2c8b0f308efa768c553375e9 MD5sum: f984f122c6cb98339f6b6f76433650b4 Description: Compare similar files with numeric fields. Numdiff is a console application that can be used to compare putatively similar files line by line and field by field, ignoring small numeric differences or/and different numeric formats. It is similar diff or wdiff, but it is aware of floating point numbers including complex and multi-precision numbers. Numdiff is useful to compare text files containing numerical fields, when testing or doing quality control in scientific computing or in numerical analysis. Package: octave-biosig Source: biosig4c++ Version: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libbiosig1, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), liboctave1, 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_1.4.1-1~nd70+1_amd64.deb Size: 24728 SHA256: 6c6aa66c92d3e5d18bb0f4f7c08ed5bef3f7182f30a2cd1b04a8d51ec4cef178 SHA1: c35cc841d919a32291223e6a421eb1717716ed07 MD5sum: 6834606b36eb816e7ad4b7f41b63cd1b 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: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 293 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, liboctave1, libstdc++6 (>= 4.6) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: science Filename: pool/main/libg/libgdf/octave-gdf_0.1.2-2~nd70+1_amd64.deb Size: 122574 SHA256: e0a171f30504237fcd1dd96a87ad19db327264a180cc5d656d51954bb18f3b78 SHA1: a2116add360c1b38634f378faffa4c8b6d4e7025 MD5sum: 792d9caa7f43dcf8e8b52bd59616ae32 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: 3.0.11.20130711.dfsg1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2755 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.7), libdc1394-22, libfreenect0.1 (>= 1:0.1.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglew1.9 (>= 1.9.0), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.24), liboctave1, libopenal1, libpciaccess0 (>= 0.8.0+git20071002), libstdc++6 (>= 4.1.1), libusb-1.0-0 (>= 2:1.0.8), libx11-6 (>= 2:1.2.99.901), libxext6, libxfixes3, libxi6 (>= 2:1.2.99.4), libxml2 (>= 2.6.27), libxrandr2 (>= 2:1.2.99.3), libxxf86vm1, psychtoolbox-3-common (= 3.0.11.20130711.dfsg1-1~nd70+1), psychtoolbox-3-lib (= 3.0.11.20130711.dfsg1-1~nd70+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_3.0.11.20130711.dfsg1-1~nd70+1_amd64.deb Size: 897238 SHA256: 2fa96b0cfcf31660a378c297d50bdfaf6b3091144089cbfef540834958ef27b1 SHA1: 1aef561cf02cc20474416c59c7dd6c3ef99cbdb5 MD5sum: d7578a228d39d5d8118c459b62fcd1ab 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. . See also http://www.psychtoolbox.org/UsingPsychtoolboxOnUbuntu for additional information about systems tune-up and initial configuration. . This package contains bindings for Octave. Package: odin Version: 1.8.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4199 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6), libvtk5.8, mitools (= 1.8.5-1~nd70+1), zlib1g (>= 1:1.1.4), libodin-dev, libgsl0-dev, libc6-dev | libc-dev, g++, libblas-dev | libatlas-base-dev, xterm | x-terminal-emulator, gdb Recommends: liboil0.3-dev | liboil-dev, libdcmtk1-dev Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/odin_1.8.5-1~nd70+1_amd64.deb Size: 1673812 SHA256: f1d36ec175c09e69222d4ebb8bfd179fea2a0853649e111f4f16abb3d6798c90 SHA1: 60bbe7a669123bf74eefbd08a4822aaa80088c10 MD5sum: 297bbd69541135573c994bc154069c32 Description: develop, simulate and run magnetic resonance sequences ODIN is a framework for magnetic resonance imaging (MRI). It covers the whole toolchain of MRI, from low-level data acquisition to image reconstruction. In particular, it aims at rapid prototyping of MRI sequences. The sequences can be programmed using a high-level, object oriented, C++ programming interface. It provides advanced sequence analysis tools, such as interactive plotting of k-space trajectories, a user interface for a fast compile-link-test cycle and a powerful MRI simulator which supports different virtual samples. For fast and flexible image reconstruction, ODIN contains a highly customizable, multi-threaded data-processing framework. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 637 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libstdc++6 (>= 4.1.1) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/openmeeg-tools_2.0.0.dfsg-4~nd70+1_amd64.deb Size: 197344 SHA256: c09fab2d3cefd845d022b5a8d13fe3adeb087133a9e39c2cb7c1d10babad7ff0 SHA1: 665303acb9e0a173b4286b8326eeb5f6395f7136 MD5sum: 32f370fe5c7f4c3cd0f823ca0f99da65 Description: openmeeg library -- command line tools OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides command line interface to openmeeg functionality. Package: opensesame Version: 0.27.2-4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25510 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-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.2-4~nd70+1_all.deb Size: 24409894 SHA256: d49d05bccbfedbe85a7bc26663be2c41238c7b58bfee3958a254b609e73a9751 SHA1: 4ee3efe2212801eaa6838a4964c473837d784486 MD5sum: f0169dcdda018cab1ebb41ae3611d202 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. Package: openvibe-bin Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1205 Depends: neurodebian-popularity-contest, openvibe-libs (= 0.14.3+dfsg2-1~nd70+1), openvibe-data (= 0.14.3+dfsg2-1~nd70+1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.18.0), libopenal1, libpango1.0-0 (>= 1.14.0), libstdc++6 (>= 4.6), zlib1g (>= 1:1.1.4) Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-bin_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 447952 SHA256: 7bb74eef7ae4ad1230b11b7bb4ac5cef5e5127f1d4de8271eb5cc1e070710096 SHA1: 21e444958d84d29880355f971c2bf0ae609c3017 MD5sum: 285d647cb96b4a366e8f8a9c514ac0fb Description: Software platform for BCI (tools and demos) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains differents executable including acquisition server, tools and demos. Package: openvibe-data Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9328 Depends: neurodebian-popularity-contest Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-data_0.14.3+dfsg2-1~nd70+1_all.deb Size: 2024456 SHA256: 7b72cf2a61f9764f3d6d4b8c632db691ffb517dcdb6d500c521b8a1eec381302 SHA1: 107a4c5c7588594034039a389571a77eb3914d1d MD5sum: b10cbfaf7110dfa2a5582c30cbe29212 Description: Software platform for BCI (Data files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the data files. Package: openvibe-dev Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 715 Depends: neurodebian-popularity-contest, openvibe-libs (= 0.14.3+dfsg2-1~nd70+1) Homepage: http://openvibe.inria.fr Priority: extra Section: libdevel Filename: pool/main/o/openvibe/openvibe-dev_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 100674 SHA256: fadf4332d3b6faef5d7642e1badbae0e6ce1b6525b794f006f08f6459339e96b SHA1: 1fffeb81109d43bfbffee2228c75357a92d3abde MD5sum: af83e6ed54ee2191fb2b54808a6b6af0 Description: Software platform for BCI (development files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the development files. Package: openvibe-libs Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2229 Depends: neurodebian-popularity-contest, openvibe-data (= 0.14.3+dfsg2-1~nd70+1), libc6 (>= 2.4), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libogre-1.7.4, libstdc++6 (>= 4.6), libx11-6 Homepage: http://openvibe.inria.fr Priority: extra Section: libs Filename: pool/main/o/openvibe/openvibe-libs_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 638288 SHA256: 42738ee9281785e614122f66d7b611292264fac53bb8300bc3c8cf62979726f2 SHA1: bb5689098455a366380f9bfc482f20ff639dd9ac MD5sum: 0c5ae2f984ccd4c9ed50796cb34c61e4 Description: Software platform for BCI (shared libraries) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the shared libraries. Package: openvibe-plugins Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5663 Depends: neurodebian-popularity-contest, openvibe-libs (= 0.14.3+dfsg2-1~nd70+1), openvibe-data (= 0.14.3+dfsg2-1~nd70+1), libalut0 (>= 1.0.1), libboost-regex1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.18.0), libitpp7, liblapack3 | liblapack.so.3 | libatlas3-base, libopenal1, libpango1.0-0 (>= 1.14.0), libstdc++6 (>= 4.6), libvorbisfile3 (>= 1.1.2), libvrpnserver0, libx11-6 Homepage: http://openvibe.inria.fr Priority: extra Section: libs Filename: pool/main/o/openvibe/openvibe-plugins_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 1663650 SHA256: ede595c32587f73c53b6a31f0c96726678e9ebe8c738e0d5b9b6d49203136ef7 SHA1: 45db90bb059f137ca063d059fd2f163189adb5c7 MD5sum: 8ef62059722ec22c84bf35314e146861 Description: Software platform for BCI (plugins) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the plugins. Package: openwalnut-modules Source: openwalnut Version: 1.3.1+hg5849-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21302 Depends: neurodebian-popularity-contest, libbiosig1, libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-signals1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libopenscenegraph80, libopenwalnut1, libstdc++6 (>= 4.6) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-modules_1.3.1+hg5849-1~nd70+1_amd64.deb Size: 6285880 SHA256: 9f8e47e2685ea57a09ab18c16b96c0ed4be9bcc7f00a998f873e62f4edfc61a2 SHA1: bb93f3bfd67cbb0a4d3448aa099b1844826fa9b7 MD5sum: a5ca6d24eda5cd28d92e8fe0a72984af Description: Loaders, algorithms and visualization modules for OpenWalnut 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: 1.3.1+hg5849-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1861 Depends: neurodebian-popularity-contest, libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-program-options1.49.0 (>= 1.49.0-1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libopenscenegraph80, libopenwalnut1, libqt4-opengl (>= 4:4.6.0), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.8.0), libqtwebkit4 (>= 2.1.0~2011week13), libstdc++6 (>= 4.6), libx11-6 Recommends: openwalnut-modules (= 1.3.1+hg5849-1~nd70+1) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-qt4_1.3.1+hg5849-1~nd70+1_amd64.deb Size: 803234 SHA256: 5bd5504b43f3ae1b5dbf307a8037bf8f6124f283a5edc0fdf522a599a0472f75 SHA1: ce963421b8a217e7a74a308d7144b056cf1cf43f MD5sum: 030f39e89e54d6fd7a82952c33ca04e5 Description: Qt based user interface for OpenWalnut 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: 0.8~nd0 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0_all.deb Size: 1488332 SHA256: 491bc5917f698fee06888998e8a295a6caac2950148bb160b457aff72437eadb SHA1: c5d75d04b01f681ead660ce8d8fe068ab887fba0 MD5sum: 8fbf7c362fd4091a78c50404eb694402 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: 1.77.02.dfsg-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9277 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, python-pyo, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.77.02.dfsg-1~nd70+1_all.deb Size: 5822190 SHA256: c10fee5b4ff9f77c68eb2e39752c47a2e8f1ba8f4510fa31adf473ddb1ff1bdd SHA1: 2f2a47167fb4144c2f470d6a919f4031727386c1 MD5sum: 32542a621060f89c5b68058c8a1b2fec 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, 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20130711.dfsg1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49324 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20130711.dfsg1-1~nd70+1_all.deb Size: 19883476 SHA256: 773c1a81c83c83ae8b3480db148e953b9d440fde91f958016f8a62562443e345 SHA1: 48341fa911dcd0e4f33159875bbf186fe1972799 MD5sum: 9b10a8dacc9553f5a03a70b2e9b759d3 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: 3.0.11.20130711.dfsg1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2695 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.11.20130711.dfsg1-1~nd70+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.11.20130711.dfsg1-1~nd70+1_amd64.deb Size: 858824 SHA256: faf1bb480e2222e9749c050da216b88be9cd5d5580a967c14b3bff0bbf79e974 SHA1: 09ba83a5e9585b677b2ef417dc442edbb08e9096 MD5sum: 28862e431a051258d0627ace58c3eadb 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: 3.0.11.20130711.dfsg1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 153 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfontconfig1 (>= 2.9.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.6) Recommends: gstreamer0.10-plugins-base, gstreamer0.10-plugins-good, 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_3.0.11.20130711.dfsg1-1~nd70+1_amd64.deb Size: 64574 SHA256: 68569cbfe33b06528d6a73cf55697e322d07524487c29eb309762badc18da817 SHA1: b8627af79715950bbc01b7d6f5989f2e05582292 MD5sum: 5867d460d3e919296c59318b4a4fa237 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: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 207 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), libbiosig1, libc6 (>= 2.2.5), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), 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_1.4.1-1~nd70+1_amd64.deb Size: 55884 SHA256: d156c47cd5914bc1f3e735fafe18f6d23bf9491166c1d55add1317635db363c0 SHA1: 4945cf58b366efc8fa4a3028bfa8b098ac907f6c MD5sum: 9642e16a5ea237553672ced757ada9b9 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: 1.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd70+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_1.4.1-1~nd70+1_all.deb Size: 549130 SHA256: 7c9586033503713d95ee640005799fd631ebc23b9857fa54739f713c13945ddc SHA1: 8df0debf188bacd59bf8e48470edc079ea401c5b MD5sum: 4d38a81ea37270a2ac871681b3c124b6 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: 1.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6798 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_1.4.1-1~nd70+1_all.deb Size: 2245550 SHA256: bbff81c2bf503166de3140452772e3684fcef15a172b12d230b1199a7333719d SHA1: 141fc07a2f6c5c65177d701a0612d7e1ba06f65d MD5sum: 99633747e0f9db0d78f345974120c115 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: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 249 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), libc6 (>= 2.2.5), 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_1.4.1-1~nd70+1_amd64.deb Size: 95922 SHA256: d71134f96e4ce240a5a547af41c69ed5b621f90e30c670833255b57f3a4a7d17 SHA1: 43491ae5e0e436252cbe6638d9b1c6c9d860f52f MD5sum: d3ca3eb38666c9097fa1c94cc9dd27b3 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~nd70+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, python2.7-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd70+1_all.deb Size: 217682 SHA256: 315d0c9976626dc452d7a4f03c9ff782c4caa12e182713db2c33d71233777b37 SHA1: 2f09d150c91742140a16fba4f03eceb4ad364e04 MD5sum: 34ba30e9fe7f1e59a608e67b241ca26c 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: 0.9.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1818 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8) 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_0.9.7-1~nd70+1_all.deb Size: 425116 SHA256: 94dbe117effdd1d6037241c583b97e0b8635d42e92b7b3a8d3bae5355de4b429 SHA1: 7d55bb2c43b2acc7c21f24465f769ffa6b26b7f9 MD5sum: f563e72d5cd375ab2a51a3e80ff81ba8 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: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2286 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd70+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy, python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.6.0-1~nd70+1_all.deb Size: 1588494 SHA256: 1f3b1b8a1d8994f60fdc5e0a281fafccef47836fae87a852d4ab2f1d1d9a0bc9 SHA1: 163f3a76886b730586c64bffc1e19d3e400252ff MD5sum: 7941b48d0f8642ecb924f0e1ee491de7 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, 2.7 Package: python-dipy-doc Source: dipy Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5068 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_0.6.0-1~nd70+1_all.deb Size: 3613464 SHA256: 56581cfaca7dc36cd0f681e04f3c495087f3f2cad28b550a4bf27e56bc93591b SHA1: 8ca22f8998d988c4b9bd996f89c4f3aae96f52c7 MD5sum: 9514848dcc0b6070ae06bcda092d46f9 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: 0.6.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1889 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.3) Provides: python2.6-dipy-lib, python2.7-dipy-lib Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.6.0-1~nd70+1_amd64.deb Size: 746370 SHA256: e3e93d880e5c7d9dba9f6bdd791023ce3deb14f99a23cedcb921bdff4381ceeb SHA1: c8c3d96c6a3c6c0bcbf411149090ef80a2d8781f MD5sum: fb446d118d6b8ddadce807ad0ab13985 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, 2.7 Package: python-freenect Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 111 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python2.7, libc6 (>= 2.4), libfreenect0.1 (= 1:0.1.2+dfsg-6~nd70+1) Suggests: python-matplotlib, python-opencv Provides: python2.7-freenect Homepage: http://openkinect.org/ Priority: extra Section: python Filename: pool/main/libf/libfreenect/python-freenect_0.1.2+dfsg-6~nd70+1_amd64.deb Size: 43092 SHA256: d093f1ea79892a4f9e181be1f96232fecef05483c7ea41cb13c4e15cc2375002 SHA1: d109bb9230d1575a6a8c922a2ab354a521756f09 MD5sum: 375f577df2577f46b241797af34c5866 Description: library for accessing Kinect device -- Python bindings libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package provides freenect extension to use libfreenect functionality from Python and includes some demo scripts. Package: python-isis Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11805 Depends: neurodebian-popularity-contest, libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-python1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libisis-core0, liboil0.3 (>= 0.3.1), libpython2.7 (>= 2.7), libstdc++6 (>= 4.6) Conflicts: isis-python Replaces: isis-python Homepage: https://github.com/isis-group Priority: extra Section: python Filename: pool/main/i/isis/python-isis_0.4.7-1~nd70+1_amd64.deb Size: 2456274 SHA256: 68944d6d3f77b7bd01128a055e9dee329e7aa3d449caebfb458bc2043811879e SHA1: 2b64501a36ba03af8a5946f3a93746233f45a216 MD5sum: eaa08181943340e25bedbc39e53f68be Description: Python bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: python-joblib Source: joblib Version: 0.7.0+git14-g3e5784c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 181 Depends: neurodebian-popularity-contest, python (>= 2.5), 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_0.7.0+git14-g3e5784c-1~nd70+1_all.deb Size: 54644 SHA256: 84652f08973583119709bb2f4f6ced550bf7d96fe4eb779fb4c919ce92a1d3bb SHA1: 98daeb1d4fb28d8507f9beeb1941556f1dcc41fd MD5sum: d228449bbddb611947f182ddd05dd9c1 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-lazyarray Source: lazyarray Version: 0.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd70+1_all.deb Size: 7334 SHA256: 72dadd7fab4a8d37309793af8b50d73a7ea93f6c223509fe58ad502936fa852d SHA1: 3a45ca7b469e524691c3ed6ec708b24bd59391a8 MD5sum: 80d3117e7a8b1fa74d6551c6f2f306ed Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1528 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git6-g7bbd889-1~nd70+1_all.deb Size: 484140 SHA256: 6928bc39f2c3b0888f5e6b236fc8cf8a677728cd26c6e7e1442cb3341a5f63dd SHA1: 900bed7f5e2e0f63a068ff302687b74ca12d92d8 MD5sum: ec7476a0bc0f232595e810a15fd1f239 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. 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. . This package contains MDP for Python 2. Package: python-mpi4py Source: mpi4py Version: 1.3+hg20120611-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2099 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libopenmpi1.3, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), mpi-default-bin Suggests: python-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py_1.3+hg20120611-2~nd70+1_amd64.deb Size: 689886 SHA256: 518a23cdf10a93e5028a71cb7886ae37d6f942c7597d01f1d68c2ae93bd421c7 SHA1: f8c815adc364b947bd5924187de59de4ca29c505 MD5sum: acd1646d5ebfc9c8867b1a9819d7933f 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: 1.3+hg20120611-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7917 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.3+hg20120611-2~nd70+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python-mpi4py-dbg_1.3+hg20120611-2~nd70+1_amd64.deb Size: 1965830 SHA256: e18c014b9ebe962baacbd6d9c191d3a56e9beb57e61c45c38c0a18e3035b4652 SHA1: 12abccc1319802e6593715c5006f3215271bcf5c MD5sum: 6979874f921a384ec126de9bdee6600f 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: 1.3+hg20120611-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 275 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3+hg20120611-2~nd70+1_all.deb Size: 76742 SHA256: a5f659c7aa363b8a241c7d2c35df15c95a321b2843614f0e503355a006582204 SHA1: 290de8ebc24c6a03b8152d6b6a9d00fe789ffe43 MD5sum: 231fb80c68d2061d1720fd4eb09ea42b 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: 0.4.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd70+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, python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd70+1_all.deb Size: 2204982 SHA256: d11d2301a31c5906b71d199f1d0c084f8b9cf9ac33bb537e24ab2b469b9099a4 SHA1: b362bf026b65424993dc7e63229b8670b55f487c MD5sum: e1bcf9e0206de77156760bbd52d0452f 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, 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, 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37572 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_0.4.8-1~nd70+1_all.deb Size: 8475162 SHA256: 650e2c780f78250bf58fada5c40a799f5b05cc59c640faac1f210075f4dc4102 SHA1: 01df95b2235666e3922f97ccfc582d42fa04e77d MD5sum: 6f013cc65b4edae93e4b62095cf568eb 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: 0.4.8-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 193 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.4.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.6-mvpa-lib, python2.7-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.8-1~nd70+1_amd64.deb Size: 72850 SHA256: 7ce7fe100b0fe479eeea64791a083f8aaa1d22742ff1e3bdcd7187c2746bd4f2 SHA1: b54c74f54e8b986dd7652e9eb3008d4fdcb40d5d MD5sum: 723c80e0cb3e08805a5014a09ebf9799 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, 2.7 Package: python-mvpa2 Source: pymvpa2 Version: 2.2.0-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4242 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-3~nd70+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.6-mvpa2, python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.2.0-3~nd70+1_all.deb Size: 2400384 SHA256: b31ea29f8331d3e354bdbc07ac0be0e6357cf3795c9ffe49e3aadc2ea468459b SHA1: 04ae7e060abba18004cd410bdc3e17e4d96791a9 MD5sum: 8b71001392abfa89e757b361f73b517c Description: multivariate pattern analysis with Python v. 2 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 PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.6, 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.2.0-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17238 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.2.0-3~nd70+1_all.deb Size: 5158142 SHA256: 2b3333a25548f7c21dedfdf980f2ed7e7f25e7dc483076683cbedd8c229bd1bd SHA1: 111b43d7bbda7bc448007a739f40c60638b11d32 MD5sum: b4cad881befe599350b711300deb04f5 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-mvpa2-lib Source: pymvpa2 Version: 2.2.0-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 205 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.6-mvpa2-lib, python2.7-mvpa2-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2-lib_2.2.0-3~nd70+1_amd64.deb Size: 77984 SHA256: 14d9a1e16026de353def4d2e59cc7b70e66b27f2e7b9de1e8603d228d771be62 SHA1: a6dd9e3ef76d5caf2d2c436ce6af9d6c354c3ecd MD5sum: e5979dcc863260e289bf231d0602d59c Description: low-level implementations and bindings for PyMVPA v. 2 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, 2.7 Package: python-neo Source: neo Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2473 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.0-1~nd70+1_all.deb Size: 1434886 SHA256: e6de592c96e96aa4ff79ef6e59890927d706f5d5802025af707db3dd8dd0b4b9 SHA1: d8dde9436d375baf70a63e8d38000d36bd97cf99 MD5sum: 4bfa684acd1ba2929c3b13629728b26a Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-networkx Version: 1.4-2~nd70+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~nd70+1_all.deb Size: 647240 SHA256: d330d947a368e24c1c211bb38680d39b541734610380b2eae4295581dc4cd792 SHA1: b2038a2f713e9b53f792369bacc2b37b26f406e1 MD5sum: 80ada5a82a23d92f2ce8d69d952d4f7f 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~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15840 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~nd70+1_all.deb Size: 6234176 SHA256: 8a284c712351861f561505f6f7a85a6d6b86732f9020951066fca67be022c7a9 SHA1: d7da2a947abc8026e87191c4ff5893cdbd013adb MD5sum: d0470a135f7b7ae6fbb4252e2b688f86 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-neuroshare Version: 0.8.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.4.1), python-numpy-abi9, python-support (>= 0.90.0) Homepage: http://www.g-node.org/neuroshare-tools Priority: extra Section: python Filename: pool/main/p/python-neuroshare/python-neuroshare_0.8.5-1~nd70+1_amd64.deb Size: 20254 SHA256: b2d421240b3cd5f8142a7565d9316643158977528ad14d2d436c1d4c7274eeda SHA1: 20692a168ed98daa38196f7d2dcf383ebbcfe865 MD5sum: f7704f38d6ed95c5f351d20c67faa509 Description: Python interface and tools for Neuroshare The Neuroshare API is a standardized interface to access electrophysiology data stored in various different file formats. To do so, it uses format- specific shared libraries. . This package provides a high-level Python interface to the Neuroshare API that focuses on convenience for the user and enables access to all available metadata and data. The data is returned in NumPy arrays, which provides a quick route to further examination and analysis. . In addition, this package contains the ns2hdf converter tool that converts neuroshare-compatible files into the HDF5 (Hierarchical Data Format, ver. 5) file format. Python-Version: 2.6, 2.7 Package: python-neurosynth Source: neurosynth Version: 0.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd70+1_all.deb Size: 32506 SHA256: e312e91ad5c3a552adf31946ac5d1c8b49511eacb9672d6d3e5fe95c67d2cf47 SHA1: 7c9148867c7d28312897d698b64bae996c8cd021 MD5sum: b003531676715edf0f1e1a459f3d6084 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4159 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.6-nibabel, python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd70+1_all.deb Size: 1826560 SHA256: d20bf8c6f53a1db782a253210648f0087c4400b973796d8320f686e390598eea SHA1: d59caeb156195f2e1402f01c2056b9c5d332b583 MD5sum: 980b87e70877228791009f66cf1d8a84 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 tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Python-Version: 2.6, 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2437 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd70+1_all.deb Size: 445200 SHA256: 05188099e45d95bae43f2ee2252dd56fcf9c846225a3a2b28d5c10a2d0373731 SHA1: 7b3495292d877b7d4521313272d64d57fedfaa0d MD5sum: 3adb28b0629dbb6cee11a5ad524ff0db 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~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1480 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libnifti2, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python2.7, python-numpy, libjs-jquery Provides: python2.6-nifti, python2.7-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-4~nd70+1_amd64.deb Size: 375728 SHA256: 7a4ac791f54109e24feabe3d4afc0be1d6dc52e7d1c78541d41bb515cca6a6f0 SHA1: 488170931671b03655c6e5db5a53e9fc9409ebd3 MD5sum: bc525d5d55673a048e1bcfb70e30c514 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, 2.7 Package: python-nipy Source: nipy Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2865 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0-1~nd70+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.6-nipy, python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0-1~nd70+1_all.deb Size: 785872 SHA256: b7ba5bef343a9b430063fd264d9613761d69bbbfad13d9a02167233650dad402 SHA1: c9f9908cc8e0a37704729d010557e12b4ddc8a9e MD5sum: de2ccfc8e8f4857a80a4697c608ad077 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.6, 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10302 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_0.3.0-1~nd70+1_all.deb Size: 3930146 SHA256: df91eb4cd8ff5cc29a288368742ff18cb2f06e73dff892aacf875a0f13716b2e SHA1: 03e2b3aac192438444be9679ddf4410e92ec3ca3 MD5sum: 8bd743d2c3a7ebdb6d54098595b1f86b 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: 0.3.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2629 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.3), liblapack3 | liblapack.so.3 | libatlas3-base, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.6-nipy-lib, python2.7-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.3.0-1~nd70+1_amd64.deb Size: 991930 SHA256: b07e7e138251da3535dbe01a8df5ecb24074e9d89d5875d83c377759e5d5f02a SHA1: 3bf34eac836ea3fe09ef08cbf1633df01c9133fd MD5sum: 9116c5a6e8a03ee131e08ecf71a34526 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides architecture-dependent builds of the libraries. Python-Version: 2.6, 2.7 Package: python-nipy-lib-dbg Source: nipy Version: 0.3.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4024 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.3), liblapack3 | liblapack.so.3 | libatlas3-base, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), python-nipy-lib (= 0.3.0-1~nd70+1) Provides: python2.6-nipy-lib-dbg, python2.7-nipy-lib-dbg Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg_0.3.0-1~nd70+1_amd64.deb Size: 1265712 SHA256: 404e7f2a1aec61d6f890700b86bf32a2d9298516eb009a60afcdef2806cc8c74 SHA1: 3ac9c0990bec6eac9bb1ad374eb38b77bb1292ee MD5sum: 6093de74405ec3154608e26b1320a55a Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides debugging symbols for architecture-dependent builds of the libraries. Python-Version: 2.6, 2.7 Package: python-nipype Source: nipype Version: 0.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2657 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, python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.8-1~nd70+1_all.deb Size: 591974 SHA256: 55589d21fb0122bc08e2e5a218f57f76372a878e7759093670dde664e33cbc48 SHA1: 5ed73db079f62e183d3dc44a020c17cf7f01b6e1 MD5sum: e81e230a669db4a93139c5cff3d888c3 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: 0.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14913 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_0.8-1~nd70+1_all.deb Size: 7058474 SHA256: b11f57c56adc40dfb58c517fecce3f39eec51e0ee36509676339603e26c9870a SHA1: 104058e90d4484d02f3b7a88ba15ba3fa3aa6226 MD5sum: 0e13f245c90c6d32870b4919e63afd08 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: 0.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9294 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), 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_0.4-2~nd70+1_all.deb Size: 3908944 SHA256: a63f5d2e326aaae30fa5fdb454b60a318a50a40393fba73dc363181011ed1659 SHA1: 1761b8e7c0fff9bb0335cfc98d091880bd84c2aa MD5sum: 897e0f8e7899efc50bd2a47eddb1d9f6 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: 0.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6833 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_0.4-2~nd70+1_all.deb Size: 5336052 SHA256: 7bba66ea7db9fb89cc4db044f9de52047d2678c203ecce6fea099fd14c4eca9d SHA1: a8fef6de47b4d9b28da4e48b28c4404cd4e794a5 MD5sum: cf70230e3055f11c4b85a0a3bf429ba8 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-numexpr Source: numexpr Version: 1.4.2-1.2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 801 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), libc6 (>= 2.3.2) Homepage: http://code.google.com/p/numexpr/ Priority: optional Section: python Filename: pool/main/n/numexpr/python-numexpr_1.4.2-1.2~nd70+1_amd64.deb Size: 286488 SHA256: f4bb8201a98d7a2bc3470eaf0d59f85c9e47394032b4ec2c4d106e06c272e2e8 SHA1: e7029211be194cb8a085d49d2cecb84933770ae9 MD5sum: 5c0afde976921ae53e9022ec72d6f828 Description: Numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. Package: python-numpydoc Source: numpydoc Version: 0.4-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 123 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-sphinx (>= 1.0.1) Suggests: python-matplotlib Homepage: https://github.com/numpy/numpy/tree/master/doc/sphinxext Priority: optional Section: python Filename: pool/main/n/numpydoc/python-numpydoc_0.4-1~nd70+1_all.deb Size: 30716 SHA256: 8648d709597fb78a38f6841b93b85eaae140766681e339038a65d36b540fb613 SHA1: f768d4b4ce1d0584845f1f240f4a0209485645d7 MD5sum: 7b6112f1cb570e854c22fc8c8ddba749 Description: Sphinx extension to support docstrings in Numpy format This package defines several extensions for the Sphinx documentation system, shipped in the numpydoc Python package. In particular, these provide support for the Numpy docstring format in Sphinx. Package: python-openmeeg Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 652 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libpython2.7 (>= 2.7), libstdc++6 (>= 4.1.1), python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.7-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-4~nd70+1_amd64.deb Size: 166148 SHA256: 807f97c09c018dcd54c1965c2bc176468f0d2f18c9c7a574481d12c4333796f1 SHA1: ace23af7d56c818dd85677ea140858ceddc05acc MD5sum: f91e2544ae3fe175e90491435ebe54f2 Description: openmeeg library -- Python bindings OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides Python bindings for OpenMEEG library. Python-Version: 2.7 Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt, python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd70+1_all.deb Size: 245060 SHA256: 19a135e4be8de62b737ca038370ef26c98892482f2291ec50c700b1ca2a5c996 SHA1: 847bd52591836b097723a48e910c63f5abb60272 MD5sum: f4ba9ac3e1c8940039fdb02678385adb 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, 2.7 Package: python-openpyxl Source: openpyxl Version: 1.6.1+hg2-g4bff8e3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 291 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_1.6.1+hg2-g4bff8e3-1~nd70+1_all.deb Size: 62034 SHA256: 3b90e8687f43ebfb82da0f9ffb48cd7bfc65547ce6231ee45b3d47722bc0d2bb SHA1: ff37abb51ce94f774f881cdacdcbcdcf6cbb3770 MD5sum: 6834ae5c32934ac734a40b4e159f0b39 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-pandas Source: pandas Version: 0.12.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5629 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.12.0-1~nd70+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.12.0-1~nd70+1_all.deb Size: 1080668 SHA256: 3cdd2f0b7d0b81b6f9a87d989419bc0b1a945d3c6c18f8b84b7d932dd4c8cc80 SHA1: f6a8c44fd29fbce463032dedd273612d237b98d6 MD5sum: 0fe4d5b9d9f36c3f00c3f27379742c50 Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 2 version. Package: python-pandas-lib Source: pandas Version: 0.12.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4132 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy (>= 1:1.6.1), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Provides: python2.7-pandas-lib Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas-lib_0.12.0-1~nd70+1_amd64.deb Size: 1519660 SHA256: b30cc2f021ab6a787198fc1e1a4836d52dc27f1b78648e5305235ff27e6a7263 SHA1: 5073665c50839a577584cdbf236a501b91e6c72f MD5sum: d32bdc68dda68ce5a8c070e8dfd1bf84 Description: low-level implementations and bindings for pandas This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 2 version. Python-Version: 2.7 Package: python-patsy Source: patsy Version: 0.1.0+git60-g7ddb7f3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 377 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Recommends: python-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.1.0+git60-g7ddb7f3-1~nd70+1_all.deb Size: 95752 SHA256: 1da5312fbe63c54723aca6c7daa4d1c1d23f70aacf58ea7d8612cfd124c01e3f SHA1: fde715bf8942afe64cd3d000c90c7d9c9c05e0b6 MD5sum: 63d481479eb5baf239764f66445551e3 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.1.0+git60-g7ddb7f3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 804 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.1.0+git60-g7ddb7f3-1~nd70+1_all.deb Size: 267026 SHA256: fa0f7bc9d5145910918d28acbf4f70594bf511d5b16e369497a815922e44271b SHA1: 85cdc18b9d7b0ef1f63e7a9cb6e145579e70f4ca MD5sum: 7803505f8a60ba2e65cd2cb4608aaa26 Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd70+1_all.deb Size: 34272 SHA256: 076297344fdb2aad569d128266cbb592689458ac0e2ec4d78a5e8ca14bf8d5b7 SHA1: 910e6bf6e2bb4575f1e378cb1af24d0f91b2bd44 MD5sum: ed9536ef265e9d7e3cd7356d561e2f60 Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.6, 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 884 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1~nd70+1_all.deb Size: 107932 SHA256: 9e2808d481734f4f0937fb9a468d30716a8eb811518d684abf9844ee21ee8a4a SHA1: d4ab6e31eadff85c7e7f8b7220cb97c7d66d303d MD5sum: 33924f1ceaba1a3a3ba22f172ff8a0d1 Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.6, 2.7 Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy, python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd70+1_all.deb Size: 21330 SHA256: af5c1ea7542c31abb491d792b1bfaef5d5a74aef7402c4659297bec687394d72 SHA1: d0b06b12f69cf46fc8a2db6c3ec5cdc548da2fe0 MD5sum: fbbf7aeb5538f3b546599d3eb9e9a81b Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.6, 2.7 Package: python-pyepl Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2050 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-pyepl-common (= 1.1.0+git12-g365f8e3-1~nd70+1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>= 1.0.16), libc6 (>= 2.3.2), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0 (>= 0.1.7), 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, python2.7-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0+git12-g365f8e3-1~nd70+1_amd64.deb Size: 612892 SHA256: ad4be2ab925f62b4fc820ee7f168075605ea6fac4b6ba8dbe599417ce8bbe5a0 SHA1: c38f81c6fb22450f87b18f090e40677c0651c4a5 MD5sum: f4a325a3a6a612b9f4b3651ae853025f 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. Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-1~nd70+1_all.deb Size: 818162 SHA256: 90aed61f6075ac5506e61e7074563588c074fbb94b6382d8f6b41149be57af1f SHA1: da93452615d933402b648f06b9e42ed025629b12 MD5sum: 72ca3455e663ab8ac1596c6cc5670169 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-pymc Source: pymc Version: 2.2+ds-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2936 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3 | libatlas3-base, libquadmath0 (>= 4.6), python-scipy, python-matplotlib, python-nose Recommends: python-tables Suggests: python-pydot, ipython Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: python Filename: pool/main/p/pymc/python-pymc_2.2+ds-1~nd70+1_amd64.deb Size: 975936 SHA256: 8cdbd6cec2d277bd676701bbfd51a807b6ab6704627402e5ad5f877fea5c9caf SHA1: 2426834b305c11825f726e1d3de2adad075251a1 MD5sum: d3c5d619042795537fdc18db9eda75d7 Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. Package: python-pymc-doc Source: pymc Version: 2.2+ds-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1840 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.2+ds-1~nd70+1_all.deb Size: 903858 SHA256: e58138742a6d440f1e36740ba231cfafd2740becf4917a1fc3554258e8a243ac SHA1: 32d42e11b09c7b7959422447a866a01cc90f2610 MD5sum: 0d85f78c49384678bdad75cbfa1d44ea Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pynn Source: pynn Version: 0.7.5-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd70+1_all.deb Size: 192128 SHA256: 3ed89b456870d6b6530e6662b034a3906298a8b612109135b96518fc3837c8bc SHA1: fa36b5bb19a5cf7b87a4fe9d12d43fccd90b1844 MD5sum: fc397ee0c6e5376bda371cc680f0c56a Description: simulator-independent specification of neuronal network models PyNN allows for coding 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-pyo Version: 0.6.6-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16037 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), libc6 (>= 2.2.5), liblo7 (>= 0.26~repack), libportaudio2 (>= 19+svn20101113), libportmidi0, libsndfile1 (>= 1.0.20), python (<< 2.8) Recommends: python-tk, python-imaging-tk, python-wxgtk2.8 Homepage: http://code.google.com/p/pyo/ Priority: optional Section: python Filename: pool/main/p/python-pyo/python-pyo_0.6.6-1~nd70+1_amd64.deb Size: 6452412 SHA256: d98343b41c185e877baf53e09506fbc8f8268c97559e8fdaade283b7d8025c26 SHA1: dece81414907ba2552ff81dc60bbe2dbd8f043e9 MD5sum: 22021d59dd81283136dc077b0d5304f0 Description: Python module written in C to help digital signal processing script creation pyo is a Python module containing classes for a wide variety of audio signal processing types. With pyo, user will be able to include signal processing chains directly in Python scripts or projects, and to manipulate them in real time through the interpreter. Tools in pyo module offer primitives, like mathematical operations on audio signal, basic signal processing (filters, delays, synthesis generators, etc.), but also complex algorithms to create sound granulation and others creative audio manipulations. . pyo supports OSC protocol (Open Sound Control), to ease communications between softwares, and MIDI protocol, for generating sound events and controlling process parameters. . pyo allows creation of sophisticated signal processing chains with all the benefits of a mature, and wildly used, general programming language. Package: python-pypsignifit Source: psignifit3 Version: 3.0~beta.20120611.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2456 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6), python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy Homepage: http://psignifit.sourceforge.net Priority: extra Section: python Filename: pool/main/p/psignifit3/python-pypsignifit_3.0~beta.20120611.1-1~nd70+1_amd64.deb Size: 690242 SHA256: a022c8575548aa7a9c33b971c2bea24fa24c495b3163922565e917278d4362ed SHA1: d6ffaffae5443e84d90207f0b89e643276c86143 MD5sum: e4c1525ece15a0887f723bbb0019470e Description: psychometric analysis of psychophysics data in Python Psignifit allows fitting of psychometric functions to datasets while maintaining full control over a large number of parameters. Psignifit performs the calculation of confidence intervals as well as goodness-of-fit tests. In addition it offers: . * full Bayesian treatment of psychometric functions including Bayesian model selection and goodness of fit assessment * identification of influential observations and outlier detection * flexible shape definition of the psychometric function . This package provides the Python bindings. 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1722 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd70+1_all.deb Size: 376574 SHA256: f3143d606791308341d10dd7752b4f8a89d4d962ddc1bfdfb43324c11b19e0fb SHA1: b35f0b369867653fb22853d37c7b2e56825267ae MD5sum: c172162c217fd132f93dfebf701445c5 Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-quantities Version: 0.10.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd70+1_all.deb Size: 62650 SHA256: 7105f0be0bad6a6896943c81ffc4f7ebd4e7ce36829bf3747f8fbb603246e059 SHA1: c36035905534efefa681ab02a9b30a297c46c3fc MD5sum: 370baf01ebbe89b0e73e46b3b3dee9e2 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported. Package: python-scikits-learn Source: scikit-learn Version: 0.13.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.13.1-1~nd70+1_all.deb Size: 28604 SHA256: f2d7aad5116e28c4442d38113f34e07c0eaff624bf8969d5525d1db7f1ad24ce SHA1: 0195b8fddb4deff62e8b709ed5aee0f738425628 MD5sum: be2931caa8f9c374c350632d2be6a84d Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.6-simplegeneric, python2.7-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd70+1_all.deb Size: 9810 SHA256: c0bf53d256b2a9520f7c40efd3af9d01c92802949256bdc3ddcbe6f8c809ba45 SHA1: b9a5abab569c8269207372b91c7e89a7230efc84 MD5sum: 46e1c70528d4fd5c5636ec720f54787f 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-skimage Source: skimage Version: 0.8.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4550 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.6, python-scipy (>= 0.10), python-skimage-lib (>= 0.8.2-1~nd70+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging, python-qt4 Suggests: python-skimage-doc, python-opencv Provides: python2.6-skimage, python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.8.2-1~nd70+1_all.deb Size: 3236984 SHA256: 4cc4e72a077f813caa0f133b07fcbf853e70be393ecd116c3b3eb2afe80ee388 SHA1: d8b3de267988fdd0cd941950805fed0f9b621ed6 MD5sum: 58c15b49a237e4bf5fbb8840c97cfa86 Description: Python modules for image processing scikits-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. Package: python-skimage-doc Source: skimage Version: 0.8.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14193 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-skimage Homepage: http://scikits-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.8.2-1~nd70+1_all.deb Size: 11823562 SHA256: 741c4e348522a251cacc1904c8b2b39eb40a94247654f3d6621245a8e3a31577 SHA1: 88c1efd39024f137d2641babf0bba0c79419fa4f MD5sum: 442a5fc7b4e0138a641dd1fa9c2be83a Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-skimage-lib Source: skimage Version: 0.8.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4722 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Recommends: python-skimage Provides: python2.6-skimage-lib, python2.7-skimage-lib Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage-lib_0.8.2-1~nd70+1_amd64.deb Size: 1741838 SHA256: 9160149a42f97ed09f68d047e50f039d2629957d945e47132fb71897d1e65047 SHA1: b675b9715e011fff045172b441d1300dde034b78 MD5sum: 9415455b45d00f140347fea21f8acd33 Description: Optimized low-level algorithms for scikits-image This is an add-on package for python-skimage. It provides optimized, low-level implementations of algorithms. Python-Version: 2.6, 2.7 Package: python-sklearn Source: scikit-learn Version: 0.13.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3050 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.13.1-1~nd70+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.6-sklearn, python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.13.1-1~nd70+1_all.deb Size: 1012766 SHA256: 80592b86ea6603b56a2ec8ec3837d6a2e9cfce132122f3b136ca43d33227daf8 SHA1: ac95a5b436322f8ba22bc40b4ef3eedc1b69cd57 MD5sum: 1e4414d8292d699d29816e6573840eec 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) Package: python-sklearn-doc Source: scikit-learn Version: 0.13.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42486 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.13.1-1~nd70+1_all.deb Size: 31108638 SHA256: a2231b3f073fa65b06ba4257d0f16fc450d6db3dcaed93b16d1231565dafa869 SHA1: 92162ad8ee6381a95fa39503bde71bf460c1130c MD5sum: 556d6137909cdb2eb772a0bee0568473 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sklearn-lib Source: scikit-learn Version: 0.13.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5088 Depends: neurodebian-popularity-contest, libc6 (>= 2.3), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.6-sklearn-lib, python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.13.1-1~nd70+1_amd64.deb Size: 1993104 SHA256: 4639c563db41e5269e4f0e73b1755fccd07dd35765e889dc55d70c38418702e0 SHA1: 4c136d44797b8ebfab8e7c1af40f879146612537 MD5sum: c877fddcaaa1b950fc36cd59511a591d Description: low-level implementations and bindings for scikit-learn This is an add-on package for python-sklearn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd70+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~nd70+1_all.deb Size: 1260232 SHA256: 648244da9a934daaee709edb7cd2d109551e93e215ebd43730a5a0bff017a035 SHA1: a878bb9a26d7085fd2ec3e02fa606ae3a44a9528 MD5sum: 9be86574fc484fd49d5be81bd6deba03 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-spykeutils Source: spykeutils Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1976 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.3.0-1~nd70+1_all.deb Size: 391988 SHA256: 750f5cb45437a4dec6e071d5d6d7f6bfa728017cada86c38e2efbc0f52f57e57 SHA1: 78a91a21f39773f9d2272c2258462294344ee313 MD5sum: 1dbd194db0abb930e8f8ab8327390da3 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.4.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12433 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.2-1~nd70+1) Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.6-statsmodels, python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.4.2-1~nd70+1_all.deb Size: 3104854 SHA256: 7a1d2cc98556f87d43ece0c055bdd3faea477245d337417bce401247cbb65845 SHA1: 96a207a7bc359d5d3a17cd6623ad3ef1d4be494b MD5sum: 8f67e249f43934e8bb54e5a87e67fa0d Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.4.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23638 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.4.2-1~nd70+1_all.deb Size: 7340256 SHA256: 0d91a4378206f49a2a1b171c51a7c8dc87fdaf6f97696c54c1007eb4b2b2401d SHA1: 73b793db2401f3e767911f5ba747ac00d83ab4d6 MD5sum: 63f2796b4e2b081f3c15f0c2168d5c57 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-statsmodels-lib Source: statsmodels Version: 0.4.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 386 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), libc6 (>= 2.2.5) Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels-lib_0.4.2-1~nd70+1_amd64.deb Size: 114074 SHA256: a81acb3bca7b13e2e8c3c95dbeab88cb7b6b1f0963b8cf69b2fea85c72f51327 SHA1: 6e7b20678ff027738f3fe163d0954332367da89e MD5sum: 04c1a3ea29b543e9b636b46e6c46a658 Description: low-level implementations and bindings for statsmodels This package contains architecture dependent extensions for python-statsmodels. Package: python-stfio Source: stimfit Version: 0.12.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 848 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, libbiosig1, libc6 (>= 2.2.5), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libhdf5-7, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1) Recommends: python-matplotlib, python-scipy Provides: python2.7-stfio Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.12.1-1~nd70+1_amd64.deb Size: 304946 SHA256: 7a64bf61733e2d2ef43af012e287bfba1b286e7e307ce895ec071a934255d4aa SHA1: 82d7e5c5c52e1bc3041949972a42d0b9b8cdd3e2 MD5sum: 53f16ba6894314da0416b5c89e5aa844 Description: 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: 0.3+git15-gae6cbb1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 95 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd70+1_all.deb Size: 28900 SHA256: 4df80f80d2fed01ef90c5a916faa87e6b6a6a8b5a3c2e659f25c1ea01ced3924 SHA1: 835637f3ec260432045cda17ac0eec30b2cc0cca MD5sum: 1b138ca525cf57a2410e515ea217bbe7 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, 2.7 Package: python-tornado Version: 2.1.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 944 Depends: neurodebian-popularity-contest, python2.6 | python2.7, python (>= 2.6.6-7~), python (<< 2.8), python-pycurl, ca-certificates Recommends: python-mysqldb Homepage: http://www.tornadoweb.org/ Priority: optional Section: python Filename: pool/main/p/python-tornado/python-tornado_2.1.0-1~nd70+1_amd64.deb Size: 223278 SHA256: 9062ef84ccd1c826e50113258d5a09ab715beb06e7e2300fb9db501d5c98b824 SHA1: 26ef56d367e2c3cd3faf2346df6e6d57e8231eb6 MD5sum: 362566030893f1c38e209b2abd9ad48b Description: scalable, non-blocking web server and tools Tornado is an open source version of the scalable, non-blocking web server and tools that power FriendFeed. The FriendFeed application is written using a web framework that looks a bit like web.py or Google's webapp, but with additional tools and optimizations to take advantage of the underlying non-blocking infrastructure. Package: python-tz Version: 2012c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 138 Depends: neurodebian-popularity-contest, tzdata, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd70+1_all.deb Size: 39000 SHA256: 4d99b0c0de79ceca4b307484afb320bed4f244d51252ae87a29f931d16f93959 SHA1: 67aa4d3871f125fa3f04b2f0fddee56d9bcdb8db MD5sum: 7766a106c9f3ea0f29222f96da871952 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). Package: python-visionegg Source: visionegg Version: 1.2.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1811 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgl1-mesa-glx, python-numpy (>= 1:1.6.1), python-numpy-abi9, python (>= 2.6.6-7~), python (<< 2.8) Homepage: http://www.visionegg.org Priority: optional Section: python Filename: pool/main/v/visionegg/python-visionegg_1.2.1-1~nd70+1_amd64.deb Size: 689474 SHA256: 443b332f8e4a13ff95dcff817a97cc4a5ad28fc0e5a62aa4156558d05a318d99 SHA1: e2bdccb2af8b7b69aa2d0dc7e4c529c8988b519c MD5sum: 7ab7660bdbcd3f93fcb96efb7626d050 Description: Python library for 2D/3D visual stimulus generation The Vision Egg is a programming library that uses standard, inexpensive computer graphics cards to produce visual stimuli for vision research experiments. Package: python-vtk Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 29686 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), libavcodec53 (>= 4:0.8-1~) | libavcodec-extra-53 (>= 4:0.8-1~), libavformat53 (>= 4:0.8-1~) | libavformat-extra-53 (>= 4:0.8-1~), libavutil51 (>= 4:0.8-1~) | libavutil-extra-51 (>= 4:0.8-1~), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libmysqlclient16 (>= 5.1.50-1), libopenmpi1.3, libpq5, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1), libswscale2 (>= 4:0.8-1~) | libswscale-extra-2 (>= 4:0.8-1~), libvtk5.8, libx11-6, tcl-vtk, tcl8.5 (>= 8.5.0), tk8.5 (>= 8.5.0), zlib1g (>= 1:1.1.4) Suggests: vtk-examples, vtk-doc, mayavi2 Homepage: http://www.vtk.org/ Priority: optional Section: python Filename: pool/main/v/vtk/python-vtk_5.8.0-7+b0~nd70+1_amd64.deb Size: 7300084 SHA256: 79cccf123b81910b7404da2470228da7f730901303fce6e8f1df9d8142398e79 SHA1: 670e7945161349fb336d6eeb74a71065dbaa92d2 MD5sum: 2c6b5f97194d746ffe6b6df8abf74895 Description: Python bindings for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This provides the shared libraries that enable one to use VTK from Python scripts. You will need Python and vtk installed to use this. Some useful information may be available in /usr/share/doc/python-vtk/. Python-Version: 2.7 Package: python-workqueue Source: cctools Version: 3.4.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 423 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8) Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: python Filename: pool/main/c/cctools/python-workqueue_3.4.2-1~nd70+1_amd64.deb Size: 135890 SHA256: 15167347dea5799e30bf991316e6bdb5f4a670a3c82be15c2e60c9d75f7fdbd7 SHA1: c28021009479d583b02107bd3aeea1171a22709d MD5sum: 3d7f7820c55367c46d2858966d0a731c Description: cooperative computing tools work queue Python bindings CCTools's Work Queue is a system and API for building master-worker style programs that scale up to thousands of processors. This package provides bindings to access this system from Python. Package: python3-mpi4py Source: mpi4py Version: 1.3+hg20120611-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1061 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libopenmpi1.3, python3 (>= 3.2.3-3~), python3 (<< 3.3) Recommends: mpi-default-bin Suggests: python3-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python3-mpi4py_1.3+hg20120611-2~nd70+1_amd64.deb Size: 348506 SHA256: c6a728ab909776db800547024e2e8761259b7dadb595ad880d30b063fb76db75 SHA1: 10075134354c00a1d553eb8a55132b1c31b5624c MD5sum: 4d553f27096f4d0efaa681afc26fecb3 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: python3-mpi4py-dbg Source: mpi4py Version: 1.3+hg20120611-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3925 Depends: neurodebian-popularity-contest, python3-mpi4py (= 1.3+hg20120611-2~nd70+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python3-mpi4py-dbg_1.3+hg20120611-2~nd70+1_amd64.deb Size: 977330 SHA256: 0c151ac1d3c5de24157f136bcaebe4ee4c32a6ac153643819ddb7cf5c2a1c423 SHA1: 566847b8fe0a016174a714378c4d1f87fa355d95 MD5sum: aee3e0bd225c7fdca9e73b6bcd6a9ae7 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: python3-pandas Source: pandas Version: 0.12.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5575 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.12.0-1~nd70+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.12.0-1~nd70+1_all.deb Size: 1076244 SHA256: 98c0a8e3a8f3cf8147f0aaa648d9ce8238c160ed94e627f730a05a4b2f1623c6 SHA1: 8a0eddd2d169a156c6f9d171e9d9a148f0f72f47 MD5sum: 95aa5c7952f90f8d7aaa94a6aa4d4b9a Description: data structures for "relational" or "labeled" data - Python 3 pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 3 version. Package: python3-pandas-lib Source: pandas Version: 0.12.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4064 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python3-numpy (>= 1:1.6.1), python3-numpy-abi9, python3 (>= 3.2), python3 (<< 3.3) Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas-lib_0.12.0-1~nd70+1_amd64.deb Size: 1483202 SHA256: 7ed4130466500965569519c05f497967733fd8848b19bdf313c22810615a21d1 SHA1: 4c552772acfd51f51abb71756b2302ef632b140b MD5sum: 5bf9b323d23ce448761ffff3af1f8464 Description: low-level implementations and bindings for pandas - Python 3 This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.1.0+git60-g7ddb7f3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 366 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-numpy Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.1.0+git60-g7ddb7f3-1~nd70+1_all.deb Size: 95080 SHA256: 811cc2bf2ab3d5c353026e251609a0006beb1be73d52128e18d60326bbbd3de5 SHA1: b79fec1bb5c83e281b0239d7c9dc5f168b44ebff MD5sum: 6d6164cbbc2ee2eafe4f9394f92838a1 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-tz Source: python-tz Version: 2012c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Depends: neurodebian-popularity-contest, tzdata, python3 (>= 3.2.3-3~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd70+1_all.deb Size: 31954 SHA256: 3e97caf66172c67dea29b32d60a6a976e032f2e3cb18dfea5ec7bb0c1a7618af SHA1: 4c06117f76e0b1ad499102b3844bd8cf2357cb7a MD5sum: 464ec516d7b9cbcf1f82127ecd56ebb7 Description: Python3 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). . This package contains the Python 3 version of the library. Package: qnifti2dicom Source: nifti2dicom Version: 0.4.6-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3099 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libgdcm2.2, libinsighttoolkit3.20, libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4:4.5.3), libqt4-sql (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6), libtiff4 (>> 3.9.5-3~), libvtk5.8, libvtk5.8-qt4, zlib1g (>= 1:1.1.4), nifti2dicom (= 0.4.6-1~nd70+1), nifti2dicom-data (= 0.4.6-1~nd70+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/qnifti2dicom_0.4.6-1~nd70+1_amd64.deb Size: 686092 SHA256: b2ed3e2a62a33b4bf4bcb282d7557aba0e14fdc83c42bc7a2ca53c7959b47901 SHA1: 0523ce9dfd3248c885b5afeb221323035f2b172e MD5sum: 14c6bf30765121f5228f57a47a55b631 Description: convert 3D medical images to DICOM 2D series (gui) Nifti2Dicom is a convertion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS. . This package contains the Qt4 GUI. Package: remake Version: 3.82+dbg0.9+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 293 Depends: neurodebian-popularity-contest, libc6 (>= 2.11), libreadline6 (>= 6.0) Homepage: http://bashdb.sourceforge.net/remake Priority: extra Section: devel Filename: pool/main/r/remake/remake_3.82+dbg0.9+dfsg-1~nd70+1_amd64.deb Size: 178652 SHA256: 71beb753e52dc4268a8cb1360128f9681ab4ac6b778303e42d069d9dcc393ee4 SHA1: a2f4e3bc0fa94c32886eac2b76ebd673217c6b0d MD5sum: 1a5b16e71b674ed5c6fb4b1bfc643231 Description: GNU make fork with improved error reporting and debugging Modernized version of GNU make utility that adds improved error reporting, the ability to trace execution in a comprehensible way, and a debugger. Some of the features of the debugger are: * see the target call stack * set breakpoints on targets * show and set variables * execute arbitrary "make" code * issue shell commands while stopped in the middle of execution * inspect target descriptions * write a file with the commands of the target expanded Package: shogun-cmdline-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 143 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libreadline6 (>= 6.0), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Conflicts: shogun-cmdline Replaces: shogun-cmdline Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-cmdline-static_1.1.0-6~nd70+1_amd64.deb Size: 45708 SHA256: 99e614a2f65d6b8db989989349e05b04a7cb6f4ef3dc08ea24ccce6f29f4644b SHA1: a1acfde2d2d54d9ba11824e222de4e769565102e MD5sum: 7857dcf2000949af764ca9e68c7bcc9e Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Readline package. Package: shogun-csharp-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7355 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11, libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), libmono-corlib4.0-cil (>= 2.10.1) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-csharp-modular_1.1.0-6~nd70+1_amd64.deb Size: 1512808 SHA256: 92802ee1447e9c260a7f77bc230e5aee023e72116cf0ebed656a9fa8e878ae97 SHA1: b70fd8e43f8dcff15e08a18730d68434580f7608 MD5sum: 3392c27ad6895e3bb25258a901845b83 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular csharp package employing swig. Package: shogun-dbg Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 72645 Depends: neurodebian-popularity-contest, libshogun11 (= 1.1.0-6~nd70+1) Homepage: http://www.shogun-toolbox.org Priority: extra Section: debug Filename: pool/main/s/shogun/shogun-dbg_1.1.0-6~nd70+1_amd64.deb Size: 16120910 SHA256: 19a2e3a2a01f472d99226761eadeee7f0590b0d87ccea1178e1c42196e7c1e09 SHA1: 1a803d0f28f806e131670da91633c1ae9fc38075 MD5sum: 757deabee3fe99b4c69b0de93680717c Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package contains debug symbols for all interfaces. Package: shogun-doc-cn Source: shogun Version: 1.1.0-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1545 Depends: neurodebian-popularity-contest Recommends: shogun-python-modular, libshogun-dev Homepage: http://www.shogun-toolbox.org Priority: optional Section: doc Filename: pool/main/s/shogun/shogun-doc-cn_1.1.0-6~nd70+1_all.deb Size: 556068 SHA256: f8376758069c8e22fedb758202fea6063d95aa3aa4400f084c4f8e10b9118796 SHA1: 3f5b5ae50cc2dcf41c120bb995369dcda3e5cddd MD5sum: 44dcec822faa27167037f325ff2be792 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Chinese user and developer documentation. Package: shogun-doc-en Source: shogun Version: 1.1.0-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 85407 Depends: neurodebian-popularity-contest Recommends: shogun-python-modular, libshogun-dev Conflicts: shogun-doc Replaces: shogun-doc Homepage: http://www.shogun-toolbox.org Priority: optional Section: doc Filename: pool/main/s/shogun/shogun-doc-en_1.1.0-6~nd70+1_all.deb Size: 17119184 SHA256: 3f07ea2441ab9f83d787f60ddb9cd08f4fc9394f062ac584ffe7e2a14e9b437f SHA1: d0333cc59cb4433eefd2ba5123fe7384b6430041 MD5sum: 4462916c2cb8bd9f994d83f46f465022 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the English user and developer documentation. Package: shogun-elwms-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 208 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libpython2.7 (>= 2.7), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), r-base-core, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9 Conflicts: shogun-elwms Replaces: shogun-elwms Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-elwms-static_1.1.0-6~nd70+1_amd64.deb Size: 61254 SHA256: b3dc6f206322cb919513ad828846d34fc623478bf505f7af0e6de1afad1c313b SHA1: 9d5e39a6f9e3d104d13eea20481345775156f99c MD5sum: 15644852e4cb04ee9b119de1ec567231 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the eierlegendewollmilchsau package, providing interfaces and interoperability commands to R, Octave and Python all at once. Package: shogun-java-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7574 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-java-modular_1.1.0-6~nd70+1_amd64.deb Size: 2247632 SHA256: ac5f4180ff5657214665e003533eb11923dd90ec0a631b15eb532eae8de1ad8b SHA1: c7da3ec3d6947e9d88dcaad075055948b87d45f6 MD5sum: 22a2d1c24fe1100268930ef759db2928 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular java package employing swig. Package: shogun-lua-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11227 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblua5.1-0, liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-lua-modular_1.1.0-6~nd70+1_amd64.deb Size: 2614016 SHA256: 6438b82eae5c423aca41f2fa03df2472bedc3eac92290071dd1c47acde91565d SHA1: a1728276cb4624e1514879c2cf5a98381527c8d5 MD5sum: 3d992952901b9fb93d31c6bcc9cef9ba Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular lua package employing swig. Package: shogun-python-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 26343 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libpython2.6 (>= 2.6), libpython2.7 (>= 2.7), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), python (<< 2.8), python (>= 2.6.6-7~), python-numpy (>= 1:1.6.1), python-numpy-abi9 Recommends: python-matplotlib, python-scipy Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-python-modular_1.1.0-6~nd70+1_amd64.deb Size: 5923306 SHA256: 7d316f9cec1676fda6f0df9864e44078052df9755481d040bfdcf43535c7c5ff SHA1: aa44f7118f66b7baac0e0747e3a93b7cee09fb44 MD5sum: dc82087d5a260cb68e405261ce9c7468 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular Python package employing swig. Package: shogun-python-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 232 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libpython2.6 (>= 2.6), libpython2.7 (>= 2.7), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9 Recommends: python-matplotlib Conflicts: shogun-python Replaces: shogun-python Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-python-static_1.1.0-6~nd70+1_amd64.deb Size: 66208 SHA256: 6caf382be4e16049d9cef7483afa01729d1b646a656fd2554ed6aa0d595a9138 SHA1: 4f107dd0b29b8a5057b6009429421b644d960a64 MD5sum: 4a1dfdedd327b095c24fb3911997ff3a Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the static Python package without using swig. Package: shogun-r-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), r-base-core Conflicts: shogun-r Replaces: shogun-r Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-r-static_1.1.0-6~nd70+1_amd64.deb Size: 65462 SHA256: 59962bed7a822cd2aed8ce5405cb2a679b35230c14a25deb54ed1a3686931ae5 SHA1: 13f03c6874369d5a42daa5914444ade604959dfa MD5sum: 64a9036ac53c3914593c1fe35acaca7a Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the R package. Package: shogun-ruby-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8969 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libruby1.9.1 (>= 1.9.2.0), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-ruby-modular_1.1.0-6~nd70+1_amd64.deb Size: 1971980 SHA256: 9b844b1f15b54779af1c14764dad3ee0799b456ec6347aa9494fd636cfd18d9a SHA1: 172a04c97937f5690926c314630ade366641a9f5 MD5sum: 3315adbba945a37febe4ee8d693633e5 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular ruby package employing swig. Package: sigviewer Version: 0.5.1+svn556-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 992 Depends: neurodebian-popularity-contest, libbiosig0, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.5) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.5.1+svn556-1~nd70+1_amd64.deb Size: 423954 SHA256: 73f66e6f5dc963f72671eb0807c52b9163643e6f50ca587bd4ab53b860f6d180 SHA1: 2baf707e4177e4d2efc7a3017d0bf8c53b214c4c MD5sum: a9b362ed51ad9f2ad059c70409156a39 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: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18626 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd70+1_all.deb Size: 10751106 SHA256: 4b0892096fb3e6c5ba1254a3c3a218a92ae151e1a37fb8fc29dadbac8b624a6d SHA1: 0397da1f5bbd5171f4ef11c705679bd2a2915530 MD5sum: 283cc17b8f9c34af894c68533fe70a57 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: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73046 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd70+1_all.deb Size: 52177460 SHA256: 51fc6055c99b93fcf82446d3357a9b8143dee566714de2921103a58a61eef981 SHA1: 11a2d79617c8c0883acdfc4e3689baf240bcdb79 MD5sum: e3fb3e6df0f60a562696f6ad2a91b292 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: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9273 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd70+1_all.deb Size: 8991102 SHA256: e203c8227771f56005d1e04f7fbec1a7bfc58c5ba9dde1da5aa8bc32f434f9c2 SHA1: a984401fdd20fa64f68b76ec1fc06d73e6ed6b4c MD5sum: 3c6e980cbe8ec3bc7f268fcb98d177bf 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: spykeviewer Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 932 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.3.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.3.0-1~nd70+1_all.deb Size: 490510 SHA256: 8bef972c5f4b13915bd7731448187b86e92496c2659b9d317c4667902df86a68 SHA1: 8e73ca6c1a40ad0a35ade595bc9e9acb70c45f55 MD5sum: 46a63ea77f88b2fb12c0e5f67cf8c186 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd70+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~nd70+1_all.deb Size: 28600 SHA256: d06a1ee5b6de6404f66db07820f084ca9699bfcef21015bb34c9cd64e1900e74 SHA1: 6515b207f33e7ef2ea59d0db40bb2b35d39355b8 MD5sum: 365f3a53daff4820e153393bb90a269c 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, 2.7 Package: stimfit Version: 0.12.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2591 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.2.5), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libhdf5-7, liblapack3 | liblapack.so.3 | libatlas3-base, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1), libwxbase2.8-0 (>= 2.8.12.1), libwxgtk2.8-0 (>= 2.8.12.1), python (>= 2.6.6-7~), python-numpy (>= 1:1.6.1), python-numpy-abi9, python2.7, python-wxgtk2.8 (>= 2.8.9), python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.12.1-1~nd70+1_amd64.deb Size: 875796 SHA256: 3fd1fec932b4434a6d33417be4bd870f93be8b700d0fd67b2467ec9bf476d807 SHA1: 42e362603527701064f7548f6ea219602d615789 MD5sum: d8bbf823bdd8ff8f10433a10ea01ff08 Description: 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: 0.12.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15195 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_0.12.1-1~nd70+1_amd64.deb Size: 4441736 SHA256: 8b5eaca638fb3d84225b928f2bb026a6f2e4a7de9b75b176f6a9460fb9baf4c5 SHA1: 0c9411002bcb5b13981dc96ec724f95352cff8bb MD5sum: 2be166f89d3dd1c51b3376a06d8d5bc7 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: 0.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-1~nd70+1_all.deb Size: 6828 SHA256: 664347bc9decb736aec4f14819a9eef0c8afedf8aae82d45087ff30facae72af SHA1: c2ca191c7b3cd09c05d737e60ed14c298dd3190e MD5sum: ac63ca302b7db2272aced98a86d44a08 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: tcl-vtk Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16157 Depends: neurodebian-popularity-contest, libvtk5.8 (= 5.8.0-7+b0~nd70+1), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libmysqlclient16 (>= 5.1.50-1), libpq5, libstdc++6 (>= 4.1.1), libx11-6, tcl8.5 (>= 8.5.0), tk8.5 (>= 8.5.0), zlib1g (>= 1:1.1.4) Suggests: vtk-examples, vtk-doc Replaces: vtk, vtk-tcl Homepage: http://www.vtk.org/ Priority: optional Section: interpreters Filename: pool/main/v/vtk/tcl-vtk_5.8.0-7+b0~nd70+1_amd64.deb Size: 5322644 SHA256: 2f787363443fa6563ed9ad98b9e2e570a354106c758c5652b4ebf23aca8e4fec SHA1: eec67a1fadd025ba3069f8bf73fb323f5c466bd0 MD5sum: 93c1f13de7e12e1be1c105180edbd3d5 Description: Tcl bindings for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This provides the shared libraries and executable that enable one to use VTK from Tcl/Tk scripts. You will need Tcl/Tk and vtk installed to use this. Package: testkraut Version: 0.0.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd70+1_all.deb Size: 100034 SHA256: 569f799af355429d7939adc34742caadb6f3eb108bb1a32b35cc5cabdb8336ca SHA1: e4a40dab2d773f92b8a810ba078d96d218775dcb MD5sum: 1a32c11b522abfa6f8b658c890f2cbe4 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.6, 2.7 Package: tigervnc-common Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 258 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libice6 (>= 1:1.0.0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libsm6, libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6 (>= 2:1.4.99.1), libxext6, zlib1g (>= 1:1.1.4) Conflicts: tigervnc-server (<< 1.1.90), tigervnc-viewer (<< 1.1.90) Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-common_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 82548 SHA256: 505ea20351136af2e78ca5874b7ba7f71dede7c08fafd7e5d7ad1a6673f63783 SHA1: 3230862b1e04a912b263cca1ba9c63d93e9b45b0 MD5sum: c4df729b6bfa12d296a3fa039eb7158d Description: Virtual network computing; Common software needed by clients and servers VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . It is implemented in a client/server model. This package provides the common software for both client and server. Package: tigervnc-scraping-server Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 590 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libc6 (>= 2.11), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libice6 (>= 1:1.0.0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libsm6, libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6, libxext6, libxtst6, zlib1g (>= 1:1.1.4) Recommends: libgl1-mesa-dri (>= 7.1~rc1) Suggests: xfonts-100dpi | xfonts-75dpi, xfonts-scalable Breaks: tigervnc-server (<< 1.1.90) Replaces: tigervnc-server (<< 1.1.90) Provides: vnc-server Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-scraping-server_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 224032 SHA256: cfe13eebecb61f5f73e90295940e05d8d2966810d7369ca3c400cf4551582952 SHA1: 16fd139d1f61395765c1e4f326dccf21f2c96923 MD5sum: 1bad73138d9c24c66d856e1e3d3a4978 Description: Virtual network computing server software VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . This package provides a vncserver which uses screen scraping of an already running X server to provide its VNC desktop. The VNC desktop can be viewed by any vncviewer even on other operating systems. . Note: If you only want to scrap your local X11 server, you should consider the tigervnc-xorg-extension package. This package provides the vnc extension for your local X11 server. The usage of this extension is more efficient than a scraping vnc server. Package: tigervnc-standalone-server Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2608 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libaudit0 (>= 1.7.13), libc6 (>= 2.11), libgcc1 (>= 1:4.1.1), libgcrypt11 (>= 1.4.5), libgnutls26 (>= 2.12.17-0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libpixman-1-0 (>= 0.21.6), libselinux1 (>= 2.0.82), libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6, libxau6, libxdmcp6, libxfont1 (>= 1:1.4.2), zlib1g (>= 1:1.1.4), perl Recommends: libgl1-mesa-dri (>= 7.1~rc1) Suggests: xfonts-100dpi | xfonts-75dpi, xfonts-scalable Breaks: tigervnc-server (<< 1.1.90) Replaces: tigervnc-server (<< 1.1.90) Provides: vnc-server Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-standalone-server_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 1176434 SHA256: 6de0a44cda867df8c32be1727d5a65fbee5a3e9fb78809f62ff6d154ee133f36 SHA1: 493c21441192a995fb574f4dadea412c576cbf05 MD5sum: 5ae8af197561408f96fccc8c46a2fcb6 Description: Virtual network computing server software VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . This package provides a standalone vncserver to which X clients can connect. The server generates a display that can be viewed with a vncviewer. . Note: This server does not need a display. You need a vncviewer to see something. This viewer may also be on a computer running other operating systems. Package: tigervnc-viewer Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1121 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), tigervnc-common, libc6 (>= 2.11), libfontconfig1 (>= 2.9.0), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libice6 (>= 1:1.0.0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libsm6, libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6, libxcursor1 (>> 1.1.2), libxext6, libxfixes3, libxft2 (>> 2.1.1), libxinerama1, zlib1g (>= 1:1.1.4) Provides: vnc-viewer Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-viewer_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 514712 SHA256: c5945488c72556a475075d4751dab111fda3e416665b10340b934efa7ad82b5c SHA1: fb36d4fe2cf5d5e588faae88b362e64c076b3203 MD5sum: 0df0484e1129d68d970638c051c62534 Description: Virtual network computing client software for X VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . It is implemented in a client/server model. This package provides a vncclient for X, with this you can connect to a vncserver somewhere in the network and display its content in a window. There are vncservers available for other operating systems. Package: tigervnc-xorg-extension Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 792 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libaudit0 (>= 1.7.13), libc6 (>= 2.11), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), zlib1g (>= 1:1.1.4) Recommends: libgl1-mesa-dri (>= 7.1~rc1) Suggests: xfonts-100dpi | xfonts-75dpi, xfonts-scalable Breaks: tigervnc-server (<< 1.1.90) Replaces: tigervnc-server (<< 1.1.90) Provides: vnc-server, vnc-xorg-extension Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-xorg-extension_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 288610 SHA256: 39f2cdd1912dc038a5f42b85ffb59417d4743e20dcba876f4505e59492352526 SHA1: 3c5e8d96beea3d24bfd14b5c84bf4ec9a38635a2 MD5sum: a1ff87dc29126eba83bd5e1faec862bc Description: Virtual network computing server software VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . It contains an X server connector so clients can connect to your local X desktop directly. Package: ubuntu-keyring Version: 2010.+09.30~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd70+1_all.deb Size: 11794 SHA256: c326d77f59c53ce386ed48a4f622087920af9c2d0a9b826e734680500b0cd3a0 SHA1: a46c68a0539f105919576423f0daeb6709e6a10a MD5sum: 8bed9b239d848186981a2e04eec03bb1 Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that. Package: via-bin Source: via Version: 2.0.4-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 628 Depends: neurodebian-popularity-contest, lesstif2 (>= 1:0.94.4), libatlas3gf-base, libc6 (>= 2.7), libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libpng12-0 (>= 1.2.13-4), libsm6, libvia2, libx11-6, libxext6, libxmu6, libxt6 Recommends: libvia-doc Conflicts: via, via-utils Replaces: via-utils Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: science Filename: pool/main/v/via/via-bin_2.0.4-2~nd70+1_amd64.deb Size: 180770 SHA256: 24267304fa1bc850db435571d21e5a6377fc6a46fc2ab6651aea60829f51f6a1 SHA1: 4d805d92442f6d4bc1596aacd2565e1eb4171e5d MD5sum: b11732d6d0c3028be0668f041559164a Description: tools for volumetric image analysis VIA is a volumetric image analysis suite for functional and structural (medical) images. The suite consists of different tools ranging from simple data handling over viewers to complex image transformation. . All tools operate on data in VISTA format. The package contains several converters from e.g. PNG, PGM or PNM to this data format and back. Package: vowpal-wabbit Version: 7.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 55 Depends: neurodebian-popularity-contest, libboost-program-options1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libvw0 (= 7.2-1~nd70+1), zlib1g (>= 1:1.1.4) Suggests: vowpal-wabbit-doc Homepage: http://hunch.net/~vw/ Priority: optional Section: science Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit_7.2-1~nd70+1_amd64.deb Size: 22036 SHA256: fd0b7d2b8375e158721a3ee8ba901186daa0ce1f06fee5e023894378f3a62976 SHA1: fd625b15a588326ab5c1cbb02b3896c61e6692cb MD5sum: d8840df3cc3b40c93f57094d0e469c2d Description: fast and scalable online machine learning algorithm Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing Package: vowpal-wabbit-dbg Source: vowpal-wabbit Version: 7.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8013 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 7.2-1~nd70+1) Homepage: http://hunch.net/~vw/ Priority: extra Section: debug Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-dbg_7.2-1~nd70+1_amd64.deb Size: 2344898 SHA256: dc98ccc63a03478563838f51c916e63b39c7b3d0903459c2b296924d50966b01 SHA1: 386b41780190a4d45cbd349e5ba84f85354ae973 MD5sum: 49e2ab6add6b6aa06b146535cbf8b6e0 Description: fast and scalable online machine learning algorithm - debug files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains debug symbols for the binaries shipped by vowpal-wabbit packages. Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.2-1~nd70+1_all.deb Size: 50202264 SHA256: 3eb654b5c4218ac049bc44a57266938153990ee8de27f301a7e9187473a44461 SHA1: 001ffa867833ab2bf4e936dba0b6df559c527556 MD5sum: 58d474d55780043657b559bcbb641d7a Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit. Package: voxbo Version: 1.8.5~svn1246-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10232 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), 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 (>= 4:4.6.1), 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~nd70+1_amd64.deb Size: 3755282 SHA256: 6bdf208f5be29c38d1a289781dc115bd8ced8ccca4666749bee93844550f6005 SHA1: 72dec9297a356f210fdea023341c40156a40fa0c MD5sum: da38303fc1cedf6c540fa94536d1a418 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. Package: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 386 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libvrpn0 (= 07.30+dfsg-1~nd70+1), libvrpnserver0 (= 07.30+dfsg-1~nd70+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: utils Filename: pool/main/v/vrpn/vrpn_07.30+dfsg-1~nd70+1_amd64.deb Size: 113446 SHA256: 30c10d6d3bb885fc839c049206fd74a92891b53e3a1dec479b4cd1beaeab96e3 SHA1: 9a7b23fc31f9c78fe54ea0dfcfe6e62f899cd5a5 MD5sum: 41c5739a785c4f86691d9bb9f130cafd Description: Virtual Reality Peripheral Network (executables) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the executables like the VRPN server. Package: vrpn-dbg Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5779 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd70+1), libvrpnserver0 (= 07.30+dfsg-1~nd70+1), vrpn (= 07.30+dfsg-1~nd70+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: debug Filename: pool/main/v/vrpn/vrpn-dbg_07.30+dfsg-1~nd70+1_amd64.deb Size: 1811102 SHA256: 12728df36d1115de7ad9b7937afa94a364869f221a24422d6b7a36af30513149 SHA1: 927f5bc1061857f298fccd6eac170612468f3be9 MD5sum: 9a06cfd29e23ff071f489c5253cd7552 Description: Virtual Reality Peripheral Network (debugging symbols) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the debugging symbols of the libraries and executables. Package: vtk-doc Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 342007 Depends: neurodebian-popularity-contest, doc-base Suggests: libvtk5-dev, vtk-examples, vtkdata Homepage: http://www.vtk.org/ Priority: optional Section: doc Filename: pool/main/v/vtk/vtk-doc_5.8.0-7+b0~nd70+1_all.deb Size: 66709864 SHA256: 1a71117b4f7574428e9da98482fb0c2cb41581e0ca6d2e931ea639a5da51263c SHA1: 74a40de489c5a44161b4aa468547d356da0bf911 MD5sum: 4c9a59935cca888f4c608d56f7eb3213 Description: VTK class reference documentation The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains exhaustive HTML documentation for the all the documented VTK C++ classes. The documentation was generated using doxygen and some excellent perl scripts from Sebastien Barre et. al. Please read the README.docs in /usr/share/doc/vtk-doc/ for details. The documentation is available under /usr/share/doc/vtk/html. Package: vtk-examples Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2521 Depends: neurodebian-popularity-contest Suggests: libvtk5-dev, tcl-vtk, python-vtk, vtk-doc, python, tclsh, libqt4-dev Homepage: http://www.vtk.org/ Priority: optional Section: graphics Filename: pool/main/v/vtk/vtk-examples_5.8.0-7+b0~nd70+1_all.deb Size: 578898 SHA256: d070189a36ffd5bed00de02b3c794d0fa8f8bb2765fbc36f0f99c1634cda5ac7 SHA1: 132096d02c71c2f969d9baff0842e4afcdbb501c MD5sum: c018d4c1cace1b218dec239a1cf5e39e Description: C++, Tcl and Python example programs/scripts for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains examples from the VTK source. To compile the C++ examples you will need to install the vtk-dev package as well. Some of them require the libqt4-dev package. . The Python and Tcl examples can be run with the corresponding packages (python-vtk, tcl-vtk). Package: xmhtml1 Source: xmhtml Version: 1.1.7-17~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 528 Depends: neurodebian-popularity-contest, lesstif2 (>= 1:0.94.4), libc6 (>= 2.7), libjpeg8 (>= 8c), libpng12-0 (>= 1.2.13-4), libxpm4 Priority: optional Section: libs Filename: pool/main/x/xmhtml/xmhtml1_1.1.7-17~nd70+1_amd64.deb Size: 256342 SHA256: 3ec43406d65e048e526712069eb321c6ffb44f9ed828b3648d9d0e7cb5bdcbbc SHA1: add62d428312df8cbfb9fbbc287535e6d70ebd27 MD5sum: 25c341b0b6a71c758df0050d3376436b Description: A Motif widget for display HTML 3.2 XmHTML is a high performance Motif Widget capable of displaying HTML 3.2 confirming text. Graphics support, lesstif compatibility and extensive documentation are amongst its many features. . This package provides the runtime shared library. The xmhtml-dev package provides the header files, and the static library. Package: xmhtml1-dev Source: xmhtml Version: 1.1.7-17~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 985 Depends: neurodebian-popularity-contest, xmhtml1, lesstif2-dev | libmotif-dev, libc6-dev Conflicts: xmhtml-dev Provides: xmhtml-dev Priority: optional Section: devel Filename: pool/main/x/xmhtml/xmhtml1-dev_1.1.7-17~nd70+1_amd64.deb Size: 345494 SHA256: fe8bf3f19ee02df02bdfedba5e22a5ae424ae89495ec24491a5f19d3e0cd843a SHA1: fc34586925edb9638c175287d3e9a856dd1ab4a6 MD5sum: d34d4ffca25f74e83a4bd4ee1d6f11db Description: A Motif widget for display HTML 3.2 XmHTML is a high performance Motif Widget capable of displaying HTML 3.2 confirming text. Graphics support, lesstif compatibility and extensive documentation are amongst its many features. . This is the development kit, containing static libraries and header files necessary to build programs that use xmhtml. The runtime library is provided by the xmhtml package. Package: xppaut Version: 6.11b+1.dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5809 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libx11-6 Homepage: http://www.math.pitt.edu/~bard/xpp/xpp.html Priority: optional Section: science Filename: pool/main/x/xppaut/xppaut_6.11b+1.dfsg-1~nd70+1_amd64.deb Size: 4187354 SHA256: 5fc94705f843f8719cc00b08626bfe7800b55fd64b26d944313e5a09984da942 SHA1: 2b7c0b88eab5942584ce1c79755424e71b8363fb MD5sum: f5b23e13b63b29d071c016e1176a554c Description: Phase Plane Plus Auto: Solves many kinds of equations XPPAUT is a tool for solving * differential equations, * difference equations, * delay equations, * functional equations, * boundary value problems, and * stochastic equations. . The code brings together a number of useful algorithms and is extremely portable. All the graphics and interface are written completely in Xlib which explains the somewhat idiosyncratic and primitive widgets interface.