Package: aghermann Version: 1.0-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1589 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcairo2 (>= 1.2.4), libconfig++9, libfftw3-3, libgcc1 (>= 1:4.1.1), libglib2.0-0 (>= 2.31.18), libgomp1 (>= 4.4), libgsl0ldbl (>= 1.9), libgtk-3-0 (>= 3.3.16), libitpp7, liblua5.2-0, 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) Suggests: edfbrowser Homepage: http://johnhommer.com/academic/code/aghermann Priority: optional Section: science Filename: pool/main/a/aghermann/aghermann_1.0-1~nd12.10+1_amd64.deb Size: 709564 SHA256: 02e13e08e8c18acecad05f14e45a4f6609d500acff147fe07b39bab0a68f01f8 SHA1: 790c314da5af774ff4c6f1c87bcc7b388a9cbae1 MD5sum: 323518ea781b35b42a81e3846d72e453 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: biosig-tools Source: biosig4c++ Version: 1.4.1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 667 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd12.10+1_amd64.deb Size: 282682 SHA256: d2b094e16b8fb5bea06912d3e553449b5054264d351f188f6836091dd4559665 SHA1: d5fec10cba69b5a0bf0272cba8260296c5e7b550 MD5sum: bddfc5782565a472503063b650c38905 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: cde Version: 0.1+git9-g551e54d-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1014 Depends: neurodebian-popularity-contest, libc6 (>= 2.14) Homepage: http://www.pgbovine.net/cde.html Priority: optional Section: utils Filename: pool/main/c/cde/cde_0.1+git9-g551e54d-1~nd12.10+1_amd64.deb Size: 367074 SHA256: 78cb629245ece108e3b817e5d79c12a7557e5a6496034e4cbecf1d1b760e9617 SHA1: 1708a732f62a1cf38375208bd0916ce722a43fbf MD5sum: 51cbda740f8efda891161370c308e2cb 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: cmtk Version: 2.2.6-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 23320 Depends: neurodebian-popularity-contest, libbz2-1.0, libc6 (>= 2.14), libdcmtk2 (>= 3.6.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.2.1), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libsqlite3-0 (>= 3.5.9), libstdc++6 (>= 4.6), 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~nd12.10+1_amd64.deb Size: 6252180 SHA256: bc6c63006c6280a21fa8f1f498cdd1de1bde3af06d2e9292e83adb2f4c167f58 SHA1: b7ad77116a9a67d1d51a66219cef966ca10024bc MD5sum: 754e5cf06a854ad7b5a34bb34c8dd0f8 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: 1.1.14-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 319 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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_1.1.14-1~nd12.10+1_amd64.deb Size: 126410 SHA256: 3b63e1b8ae9b04283ee69a20095c540cb3c2eb1daf22d34c0290cae043685655 SHA1: cbf70da924966860a1532940dc940f897947922e MD5sum: 12f8de9ac2bc99f7355ec14b6e64c21d 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 14724 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.14), libclassad3, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-common0 (>= 14), libglobus-ftp-control1 (>= 4), libglobus-gass-transfer2 (>= 7), libglobus-gram-client3 (>= 12), libglobus-gsi-credential1 (>= 5), libglobus-gsi-proxy-core0 (>= 6), libglobus-gsi-sysconfig1 (>= 5), libglobus-gss-assist3 (>= 8), libglobus-gssapi-gsi4 (>= 10), libglobus-io3 (>= 9), libglobus-rsl2 (>= 9), libglobus-xio0 (>= 3), libgsoap2, libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.10+dfsg~), 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), 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~nd12.10+1_amd64.deb Size: 4912354 SHA256: e95001d6da8f014314bb44c89ad32c9f0d35e3f019f3bde2ed5f947b64c9cb95 SHA1: 0ebd11d71aa5341ace22c6ff639b941719242721 MD5sum: 803a8fbb1a7272006ea21ad11b09e13f 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 35984 Depends: neurodebian-popularity-contest, condor (= 7.8.8~dfsg.1-2~nd12.10+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~nd12.10+1_amd64.deb Size: 12414292 SHA256: 344129a28c01e1622573cdd89230bae124fd62ab663d49f92e8b91ca799aaef4 SHA1: a0692373d0febe4457da5211ae2448c359927458 MD5sum: 9fded91d6486d1b18579b06db571cca8 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 2068 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~nd12.10+1_amd64.deb Size: 459930 SHA256: f0ef4bb98bbc8a2413d7df8e0af91dbf547f778f5d65af747b236ab3bd160225 SHA1: cd572ed296d4af75d6ed0f5938bc13814d7314ad MD5sum: eaea10339c9f2c69f43ae9d1ed4f242c 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6118 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~nd12.10+1_all.deb Size: 1459068 SHA256: 70cd94e29bc3de42c5c146f1f9f5ffb1aad113b7960a666463ab2ddfa1b71f27 SHA1: 30b14a0fbfe38945913718bf73a4481068433dc8 MD5sum: 51eea58a6838ad510d31a1e698453a84 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: debruijn Version: 1.6-1~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 147 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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~nd12.04+1+nd12.10+1_amd64.deb Size: 48052 SHA256: b78405fd5c9706c38b65244862e9f2a0b3e076779b3cac0346b96c61dad64063 SHA1: 2072ad81e6367eb0f7382d9073534d64ad7fc0b5 MD5sum: 4e314db6a5b8595b1d4f05f5171c8c16 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: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+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~nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 7224818 SHA256: 25bbf59e6baaa0fd1f795f650fc89e2fc7f1c9bed1172b1adfe766a6a9b64be4 SHA1: 5b471b69135beae6f699377fdfcb606d1fcb972e MD5sum: dd4f89591443db2aab3bfc912c908f2e 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~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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~nd11.10+1+nd12.04+1+nd12.10+1_amd64.deb Size: 10998 SHA256: 99be780fef25795fbe1d64c2024749af49de894021e1bea3d14ae57a9d82a826 SHA1: d5caf3eef038a0bad0c8fde49cd6486633654906 MD5sum: 7f5b2d17ebe6df55e920758f8cef1100 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: 0.8.11-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 525 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), 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_0.8.11-1~nd12.10+1_all.deb Size: 176810 SHA256: 593753beefca6dae0a4570cac78e3f3e62cfff7fdd8922a222ea3e534f33f331 SHA1: fd2daf62594dd1f765db9a47051d4c0862475660 MD5sum: a104aacfae196aeae2fb54c651ca00ad 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: fslview Version: 4.0.1-2~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6520 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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~nd12.10+1_amd64.deb Size: 2331628 SHA256: 847006dc259f5999c58341488061b7e41030b6cb4ca16356707a4e27ad389349 SHA1: c2d2f1edbc4ba5448aa92f10ab43b828cdb6b7b6 MD5sum: cdb10149c554a89c4a521f45a87ebd84 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~nd12.10+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~nd12.10+1_all.deb Size: 2346536 SHA256: a6eadbe29e5145806b86d93ceb70677028343bbbe7d68af97c8511c3d75d9668 SHA1: cdf1cc15b93159e998a9279d80a5c148995cd5cf MD5sum: ed6f5a1eedcf462bbdd786a35b032a08 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: glew-utils Source: glew Version: 1.9.0-3~bnd1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 409 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd12.10+1), libc6 (>= 2.4), libgl1-mesa-glx | libgl1, libx11-6 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~nd12.10+1_amd64.deb Size: 126134 SHA256: a4422a20b8fe44f2f07f4fa331ea17a40d191819a2fc80e4e17e4e202a77ca94 SHA1: 490615af03561bc861ddb998f0d852b947143dda MD5sum: 2d88d0c5bd80c88124c1f10a1eea25ac 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~nd12.10+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~nd12.10+1_all.deb Size: 16312 SHA256: c624ce3d8442fdac91d05990f58947773022cee861525c83520f6bd9db41d426 SHA1: aa9d4e0aae7f43288022aaa829c609240669cb34 MD5sum: d209e936af7489348d8b121e088e972f 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 Source: guacamole-client Version: 0.8.3-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 475 Depends: neurodebian-popularity-contest, guacd Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole_0.8.3-1~nd12.10+1_all.deb Size: 428588 SHA256: 9fe31295bdb7f985557eef13d55fdc2f9d2420edb3a32fb3e6217508d88fddde SHA1: f92e6e61aa0b69d6aeb2a6800f5a5074e9ac1ac0 MD5sum: 1cd439407b3d83fb5a4f8a1061627c39 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-client Version: 0.8.3-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole-tomcat_0.8.3-1~nd12.10+1_all.deb Size: 6948 SHA256: 5c0bf2422be06b3517798b82f880d9201c307a3ddf3cd40712ac96ec8721c45b SHA1: a1232d96884124049e2db55b9fb8efb06cf23324 MD5sum: 420333707c4a6d9dc554374310de3ae9 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 Source: guacamole-server Version: 0.8.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 33 Depends: neurodebian-popularity-contest, lsb-base (>= 3.0-6), libc6 (>= 2.15), libguac5, libssl1.0.0 (>= 1.0.0) Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-server/guacd_0.8.3-1~nd12.10+1_amd64.deb Size: 15478 SHA256: bfb67e1740e3a23b509e3a18a4f7af53c4b9afffa8e8db246c88ba8c968d56ff SHA1: 8a94dfacda8450ce80387735502d245f3be9537a MD5sum: b16387408f0b55da03c2e08b80b5fcaa 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: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+1+nd12.10+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~nd12.04+1+nd12.10+1_all.deb Size: 9726 SHA256: 34850e6858d784f40edaa883e66923b867c1262d92203a3ccde4cd38fc505897 SHA1: efa6a60304adb482d61201f9187f1fb23807d12b MD5sum: 0f86d558162919041ff81fb2e7129410 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~nd12.10+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~nd12.10+1_all.deb Size: 2408046 SHA256: f305f9f5f32eb0cea86116b9f9d34e45db54bd58624c88d66c5cfba336057917 SHA1: 6fdcb1f6c217cad141efaba3f65e29de6cb75ffe MD5sum: 58d52adfb2463cbcff45c428a3b9dd59 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4665 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7, python (>= 2.7.1-0ubuntu2), 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~nd12.10+1_all.deb Size: 1286312 SHA256: 0582d740bc1f1a33af6a517f3511e3a66a52dbdcd00890473a96edc5c1a4f293 SHA1: 51bf8ad39c9f17cd47810354efe508807b0b53a6 MD5sum: 47a6395e7135f8958d2b1f7efaf874c2 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16663 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~nd12.10+1_all.deb Size: 7237370 SHA256: 901a02593af0166028b3e79042c6ad3130382b573bd332cc0219725f42049cd3 SHA1: d01041ef99be7cbacf2e202edb4855294f66a49b MD5sum: b52f37cc8eff0d3f508b0abd5794c8c1 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~nd12.10+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~nd12.10+1_all.deb Size: 898 SHA256: ca759cce540d54f6faf0b3c3f024cde830e8f4afee0f0081a627485dd83b51ec SHA1: 4fb2e204a1cce5b1053949570576995682d2f190 MD5sum: 8fdd7c8bd7fed9054acc92b9648a231f 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~nd12.10+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~nd12.10+1_all.deb Size: 826 SHA256: 66d11d9ddd9ce8b7982ebb1f21efb41203b441715e83674c3d54d0a03500280b SHA1: 1791024f575769c9162827490cbbce02e92fa708 MD5sum: 159dc968ca74420c8f1f4835a9619370 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~nd12.10+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~nd12.10+1_all.deb Size: 908 SHA256: 28d34754ea400f26f869a25832d9e62f075f1a8c7773410823781db08a990511 SHA1: 61e9b6c0a2c6c81aa181a5472faa82368c94bd49 MD5sum: 1ffc5191748f4538a3938123150e4953 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: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11748 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython1x-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/ipython1x/ipython1x_1.1.0+git7-gf5891e9-1~nd12.10+1_all.deb Size: 4486410 SHA256: 736400f7973324f547cde68b4495c695df3be455d21316441abef34b94f30c8f SHA1: 11439c8742beac83c18694028b08831c8e9dbde0 MD5sum: 64c040b8244d5905cbc5cb440014eda1 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 IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10402 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.1.0+git7-gf5891e9-1~nd12.10+1_all.deb Size: 4197232 SHA256: 54f15949e35b176dc76b435868d4b41be25d7bef03de177b74e8d810378cbabf SHA1: e56a1947f903519397290718e35473e086fad8b8 MD5sum: ecb6af94b7bf4556a1a6ad52065af551 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 IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.1.0+git7-gf5891e9-1~nd12.10+1_all.deb Size: 914 SHA256: f060593ed7aaaaa70c57d2ad3a4da77596bdd88d13ce4d2a09a822a648ce7ba7 SHA1: 2c687e1f890b6002bfb0874b9b1edb428692a996 MD5sum: fc65a087e74f388c657ff76a5c29e40e Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x 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: ipython1x-parallel Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.1.0+git7-gf5891e9-1~nd12.10+1_all.deb Size: 840 SHA256: 1c5fe5d12434f7ecccbb99781d07fba227d3d63e57588fdbb73c1c512c33ea2f SHA1: 068a3f7dc68de828844e22337bf7c66866851644 MD5sum: ee4e8a9b9f7da8c5c35f1ea3574892cb Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.1.0+git7-gf5891e9-1~nd12.10+1_all.deb Size: 926 SHA256: 50c17367c4fbc48221f18b06766d93acee59eda00e3cd333e6974d153e745eaf SHA1: 8c35c5da8f0b2f41492fd1c9754a30feb5f8c603 MD5sum: 9a930750646f00932add092379abc9d6 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x 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: libbiosig-dev Source: biosig4c++ Version: 1.4.1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1708 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd12.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_1.4.1-1~nd12.10+1_amd64.deb Size: 425832 SHA256: 780d94fa348f10c496093620610831049ae130a923505412a61de2eb6ccb0169 SHA1: ec89cde7e13903c3b3a79df5c8e792f835f1115c MD5sum: 74f765b2d608e57491ecf06b30816d22 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 909 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig1_1.4.1-1~nd12.10+1_amd64.deb Size: 336160 SHA256: e2b53bacd6772d2b61c2c71e4ac865ee2969a4e026340402534a65be9bed7014 SHA1: 6df004788795a1a5428528334fa344f879164e3e MD5sum: 6b1dac68e80a45ae8aeb16282c5720fe 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 378 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd12.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig1-dbg_1.4.1-1~nd12.10+1_amd64.deb Size: 118566 SHA256: 5604f8ac90c359005bce28841065ce24054e21b2f601e565a6adb11230eb30fe SHA1: 6d65cfa49b8465f603f986292a113265ab5fbaf1 MD5sum: ccb4423cbbdb5007719b84465a25c2a0 Description: I/O library for biomedical data - debug symbols BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides debug symbols. Package: libclassad-dev Source: condor Version: 7.8.8~dfsg.1-2~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 2834 Depends: neurodebian-popularity-contest, libclassad3 (= 7.8.8~dfsg.1-2~nd12.10+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~nd12.10+1_amd64.deb Size: 523608 SHA256: 1052fff1a404b8cec73d80afd4f747dab5abf7ebfc7e187e079649d72a5cd535 SHA1: 8b385d635a93c566ec77bd1e52b5fce150064a20 MD5sum: dcfd257195160b290f720cc9bb0edb1c 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 912 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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~nd12.10+1_amd64.deb Size: 275366 SHA256: 5636cfad2683da3488af8398f037f4ef5bf9b8f2a16e2348912563793150b44e SHA1: ed6629ede9e955d41237cd8c48f881c375ff8a95 MD5sum: f172bb56070a53aca25b1f5a8898d328 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: libglew1.9 Source: glew Version: 1.9.0-3~bnd1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 632 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1 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~nd12.10+1_amd64.deb Size: 174096 SHA256: 3276e5b6aa7b5e19367fca51ee480588da2c7fd637712194bfb6c6f39c39cae3 SHA1: 6cef7f5f08f7dd548a24dc511582842371a80cc4 MD5sum: 733a47f0283cb40826d0c8148b9d2113 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 150 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd12.10+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglew1.9-dbg_1.9.0-3~bnd1~nd12.10+1_amd64.deb Size: 41096 SHA256: 1d997a16659ef7ac4ccfb6c0bbd66dc107763c45b37b8a8be036e8c87443ce7c SHA1: 240e90f04288d891cd65451f1a1b07b527b198b2 MD5sum: 786145ecae4db1e70b704a70a5d42a30 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~nd12.10+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~nd12.10+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~nd12.10+1_amd64.deb Size: 153218 SHA256: 767f99e90d2038ccc1064cb9f2c4abdf7a63d762cd537ad7c6d77586246cfe18 SHA1: c2049321c42527342ac05f56b85dc757c908489c MD5sum: 7a1eccd86d5ebc282b1fba9bcd57497c 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 559 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1 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~nd12.10+1_amd64.deb Size: 158310 SHA256: ec7d748cd97973a7f2033cffa3bf90fdf416963d44d8bc6b29c8597a12f500c4 SHA1: 9d6cbf16bdf279cb6bacd6dfa89833973a9c4a67 MD5sum: bbaa158af7e404443e24950dbc208745 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 122 Depends: neurodebian-popularity-contest, libglewmx1.9 (= 1.9.0-3~bnd1~nd12.10+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglewmx1.9-dbg_1.9.0-3~bnd1~nd12.10+1_amd64.deb Size: 33076 SHA256: 1fa5bb14db6e279110f70ddf035e24d637b32cf4237dae894e50662005598b7a SHA1: 005201e6b78800665d31fc9a6032beb3ea6214d6 MD5sum: 857adefdaa57bbe06524298ede4ac2e0 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libglew1.9-dev, libglewmx1.9 (= 1.9.0-3~bnd1~nd12.10+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~nd12.10+1_amd64.deb Size: 8818 SHA256: 705d6c266cde0fff55d8d39dc354a9f0801f1b989acc812610859ed13cb37870 SHA1: 81fd31c7686be38bfdc223ae5595d02af00d7638 MD5sum: cbdd36576af20c6673e90ba685149dbe 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-rdp0 Source: guacamole-server Version: 0.8.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 104 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libcairo2 (>= 1.6.0), libfreerdp1 (>= 1.0.1), libguac5, libogg0 (>= 1.0rc3), libvorbis0a (>= 1.1.2), libvorbisenc2 (>= 1.1.2), ghostscript Recommends: libfreerdp-plugins-standard Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac-client-rdp0_0.8.3-1~nd12.10+1_amd64.deb Size: 36204 SHA256: 6f8d765a41e93e372b7751c25c5f173ba6833d3eef1b2a07bb1bb93f3b6a30aa SHA1: 01fed340efd90f079e1176bd1bef3b2f3552c5f8 MD5sum: 479a69eb43d610f2a5a6a1abaf4c7227 Description: RDP support plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the RDP protocol (Windows Remote Desktop). Package: libguac-client-ssh0 Source: guacamole-server Version: 0.8.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 52 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libcairo2 (>= 1.2.4), libglib2.0-0 (>= 2.12.0), libguac5, libpango1.0-0 (>= 1.22.0), libssh-4 (>= 0.3.91) Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac-client-ssh0_0.8.3-1~nd12.10+1_amd64.deb Size: 25634 SHA256: 7e4778744b4f92d59252a9a0b4e990401e2f95f559b204d15864e393b564cf33 SHA1: e21927406ed7ffd2bef95c2e2c51a957b109e4da MD5sum: 34779cc4e962d5553c198499d275c533 Description: SSH support plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the SSH protocol. Package: libguac-client-vnc0 Source: guacamole-server Version: 0.8.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 27 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libcairo2 (>= 1.6.0), libguac5, libpulse0 (>= 1:0.99.1), libvncserver0 Recommends: vnc4server Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac-client-vnc0_0.8.3-1~nd12.10+1_amd64.deb Size: 11954 SHA256: eadb4e20a494b0c892327fae94a0086f7c685a01c4dbd9814a199ff0b8b78fbe SHA1: 55e188112d2f74fe21d8728cc1876d2dec649c40 MD5sum: 39b3f1262a7dbb0e30696ad21cbf9131 Description: VNC support plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the VNC protocol. Package: libguac-dev Source: guacamole-server Version: 0.8.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 214 Depends: neurodebian-popularity-contest, libguac5 (= 0.8.3-1~nd12.10+1) Replaces: libguac1-dev Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libdevel Filename: pool/main/g/guacamole-server/libguac-dev_0.8.3-1~nd12.10+1_amd64.deb Size: 45102 SHA256: e3517cd70b770c1876b3398b2d9355849feebd9a9e4b82bfb81e0a54161b7c80 SHA1: fa3cb149ca736a4314ccb72b37d6db2058610a5a MD5sum: 5050d3299a8faffdcf7d25c6c9730b0e 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: libguac5 Source: guacamole-server Version: 0.8.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 59 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libcairo2 (>= 1.2.4), libogg0 (>= 1.0rc3), libpng12-0 (>= 1.2.13-4), libvorbis0a (>= 1.1.2), libvorbisenc2 (>= 1.1.2) Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac5_0.8.3-1~nd12.10+1_amd64.deb Size: 25138 SHA256: 12e59fda94673b0d443e371e9b1b9b3da3c58706f235c2a691388db1bf2b4098 SHA1: 0077c5a2857141501a71e9f74b19475fdc5800c7 MD5sum: 8a56521ee096427c68f395d9f35d906c 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25773 Depends: neurodebian-popularity-contest, libinsighttoolkit4.2 (= 4.2.1-2~nd12.10+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~nd12.10+1_amd64.deb Size: 5275654 SHA256: 8630bf977cbac9847f94a3253b948dea5ba7befcca999fdf5e7403f7f72f9d50 SHA1: 0bd13b73fea06814e078a4e6b1262e9b41953c45 MD5sum: 98ea8fa17f3ebd835dc45f33651fee74 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21923 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libgdcm2.2, libjpeg8 (>= 8c), libminc2-1, 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~nd12.10+1_amd64.deb Size: 7145742 SHA256: 89a5bc2672aeca8b6d54f830bafa18fefccde1255e504b02e168182ae77f8b0e SHA1: 180a09674012c4ba27a71dbb7f1ad1b11cd82026 MD5sum: ae689ac5f3e62bd0e0258ba713e4f6ec 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: libmcpanel-dev Source: mcpanel Version: 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libdevel Filename: pool/main/m/mcpanel/libmcpanel-dev_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1_amd64.deb Size: 2402 SHA256: ecc2c9ce038a1b19411f89ad673c00677fa0d7334543da1e90db12886ed635b5 SHA1: 46de4b6ad096faf310e3711e0ebf268fb3302492 MD5sum: 3f3737ed635126a5f1d08ceb24fe3e78 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~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 140 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.31.8), 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~nd11.10+1+nd12.04+1+nd12.10+1_amd64.deb Size: 49138 SHA256: 9ddbf9361a5559d529c2ecc6529fc2813b6cc984e81ac0ab0fe96508f5405406 SHA1: 57eaf81dc01db696236f487cab07b95a5475b7a5 MD5sum: 857664762f1b92c862305c31505ab639 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~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: debug Filename: pool/main/m/mcpanel/libmcpanel0-dbg_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1_amd64.deb Size: 6722 SHA256: 7fa3c2f653d4413ed0d734bac62a3e152be16b42be1ae7e92f8ec9f647034293 SHA1: 4fc0ea3f52569209dd28c905fcbc4d4d37b91e8c MD5sum: 4ae5f52eed8e4463e0aa636ba45fc66a 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: libopenwalnut1 Source: openwalnut Version: 1.3.1+hg5849-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6366 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 (>= 2.14), 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~nd12.10+1_amd64.deb Size: 1747262 SHA256: 693d35583aecb51384753e5ed635887cc0e3b643581b4f5a5827074306d8ad66 SHA1: 40751a995168faba09be504228a543ab3d4e7065 MD5sum: 88abac4eca0f5216ec6eb1dc7aba573e 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1797 Depends: neurodebian-popularity-contest, libopenwalnut1 (= 1.3.1+hg5849-1~nd12.10+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~nd12.10+1_amd64.deb Size: 304162 SHA256: 4857abf5eca8851b9298dea71476ac98b6312d7b7622965d79b88b31f686cfcd SHA1: a65edef78db41b374e22832464dedab4d01ed2dc MD5sum: efaa986e749496c44c54fcacf92505e4 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~nd12.10+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~nd12.10+1_all.deb Size: 4548686 SHA256: 7471480f54b77725a0fd6d1c3d06ccb0daff4a5339c64af859a685eff5510d1d SHA1: 36e33879a41875780c35bef7c0c42c62cd2488dc MD5sum: 1d78db66fbcb7c5cee14c3a5f124916e 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: libvrpn-dev Source: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 659 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libdevel Filename: pool/main/v/vrpn/libvrpn-dev_07.30+dfsg-1~nd12.04+1+nd12.10+1_amd64.deb Size: 174136 SHA256: 1dec69716ad91c008bf8fa6b98a35dc6253f53344745c82736620cf0ea39c592 SHA1: 61078a436788fb4302831045b5f2e7afecca0c74 MD5sum: 9854e6264b7acc40ca24660880900178 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~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 535 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), 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~nd12.04+1+nd12.10+1_amd64.deb Size: 182116 SHA256: 55902640dd8606b3e9d53a6b46fd16337a42ea610978d6ba8a7fcc296bbb2a98 SHA1: c934db7e36a2de9be933ba2ad2e0ff637ca9a40a MD5sum: 2af6d9d6faface3011006b06580ee322 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~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1355 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), 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~nd12.04+1+nd12.10+1_amd64.deb Size: 437386 SHA256: b7d37721dde4e1f75850ee5c7119426afa3822d9642dbd8b5fcabf91e82093c8 SHA1: fecd69d00ce34d0e8dfa20c2411969e8eee85cb0 MD5sum: b66293205631c5400431a515a4745102 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: libvw-dev Source: vowpal-wabbit Version: 7.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2577 Depends: neurodebian-popularity-contest, libvw0 (= 7.3-1~nd12.10+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_7.3-1~nd12.10+1_amd64.deb Size: 580014 SHA256: 727243c0dee8323252e365d05176829b880784ca8194ac0dca24ce2b7e9632d9 SHA1: baa21a389e84039e0e64d9a41b07b4ba81306d1a MD5sum: 79f81eee3e42f04c052bf25e625e8184 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: 7.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 726 Depends: neurodebian-popularity-contest, libboost-program-options1.49.0 (>= 1.49.0-1), libc6 (>= 2.15), 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_7.3-1~nd12.10+1_amd64.deb Size: 305590 SHA256: 2165180e7d3b01d92a1d830eac0bc56d8d74f83bf35b62afee156b47f8e2529d SHA1: ab9253116a206f0e4a567c811ccfbc74d9cd39fd MD5sum: 95e8c0cbe258cb76b1bd79be265c5296 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: matlab-support-dev Source: matlab-support Version: 0.0.19~nd12.10+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~nd12.10+1_all.deb Size: 7226 SHA256: 3b786fa3329b2dba487558a85109d9045b41d99dd546eb01be7c9e6050850421 SHA1: 18fdd673cdfc665496abe1840fc586a3453a4a4d MD5sum: 89d8df01031330fa00c9d7ebd3851bb2 Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mriconvert Version: 1:2.0.7-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 3205 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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_2.0.7-1~nd12.10+1_amd64.deb Size: 1021138 SHA256: 38d2deac5f580c94f99752c08b65a649da5055160e84a2753700eee0ead5d43d SHA1: 01ce751ba70efa7e22df3540f36cd940d4717366 MD5sum: fb2efc34cb32568367714915b3ffa0f6 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: 0.20130828.1~dfsg.1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 19646 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_0.20130828.1~dfsg.1-1~nd12.10+1_amd64.deb Size: 6408098 SHA256: 25920428d0b8434035f94ee337bfa74fd3a2dc347ab3000aedcaf827d9fda8eb SHA1: 9d22bbe3b39da97b08b5085bc8c0f98297ba7f83 MD5sum: 99804ab1051179bcc1a05edfda31f1ab 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: 0.20130828.1~dfsg.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1710 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20130828.1~dfsg.1-1~nd12.10+1_all.deb Size: 1664526 SHA256: 3fb87f58bf34e7b888148d05c9062ed174dfc6d67eabef8e7197f01049a4ddc5 SHA1: aa35d50b5549bebfbb76b31d102c6f68ed99f0c5 MD5sum: 7f5cd27752b5330ca67b207bf2c42e0c 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: 0.20130828.1~dfsg.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1019 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20130828.1~dfsg.1-1~nd12.10+1_all.deb Size: 737512 SHA256: c5c488fa91d85838c86c7fa26a46cca8f62c97d7f53cd1ca26d191739c15ec10 SHA1: 6560362c05712ac4eec11eed1039e9b3a20a32a7 MD5sum: 570ce9919852d15fb456e7d71521d8d3 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: 0.2.11-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 8379 Depends: neurodebian-popularity-contest, libatkmm-1.6-1 (>= 2.22.1), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.33.13), libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1c2a (>= 1:2.24.0), libsigc++-2.0-0c2a (>= 2.0.2), libstdc++6 (>= 4.6), zlib1g (>= 1:1.1.4) Suggests: mrtrix-doc, octave, matlab-support Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: science Filename: pool/main/m/mrtrix/mrtrix_0.2.11-1~nd12.10+1_amd64.deb Size: 2621368 SHA256: c6cf7aec549def4e6e0b4d15b0aa7c0a6a2ddc97daf55d33382c65a076f68854 SHA1: 796ef6aa10e9106de9ca465378ad16774a4e2407 MD5sum: d586b7fde8a985c08f519dcec9ce8acb 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: 0.2.11-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3520 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.11-1~nd12.10+1_all.deb Size: 3314700 SHA256: 8ca8206426ea601736db4a226cbf4be3228016d9d8d464fc69bdc723aa543c02 SHA1: 47150b0ef6aec45af967fb4a3d6972c2ebf5c8a6 MD5sum: 1f8da23125227c6aa240dab5922653c2 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.32~nd12.10+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_0.32~nd12.10+1_all.deb Size: 115544 SHA256: 997c34e18532f1519f964fd0f01922cd79cebd8ec803ff30d17a583424f5987f SHA1: 983c2f829d1c86a1a697e4465dad3645cf671a5f MD5sum: 3a1082a75bcd3c2a61aa2de30b93ff49 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: 0.32~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6842 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_0.32~nd12.10+1_all.deb Size: 6433058 SHA256: 0bce63bec60ba98190e62fd00b771900a9bdc481843cdfd5e3040ceea7a69209 SHA1: 434ffe9db1293cb480ac3ecde1eacccf99e5b2ec MD5sum: 7886f3d9151d4a846797d37abe29d831 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: 0.32~nd12.10+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_0.32~nd12.10+1_all.deb Size: 15356 SHA256: 3feaa8729d9810220475cc190e248c0641f1df7882b3f90e31a53e53c0153a10 SHA1: 47c193c7a0457a6ae2acb616fae239e3b85bfd3a MD5sum: fc60a300c919fc123d763c7d353dda7c 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: 0.32~nd12.10+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_0.32~nd12.10+1_all.deb Size: 7620 SHA256: 00714ca2abc1fbbeb3e20a4ab02e525c76702c31ee0e5f1c6cd4910dffffd804 SHA1: 3e4da753b269fe942bf7a7cc98529d0c50580cbf MD5sum: c3280db44a03f7a24432ef4a1f2332a8 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: 0.32~nd12.10+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_0.32~nd12.10+1_all.deb Size: 6842 SHA256: 2ea6b014fede56401405c49d0e20fc4fd0f032635c5b835d8e6181a0221f4fe5 SHA1: 0d6aaabd6c49940debbeeb9881efb294d569ebd5 MD5sum: a992c797281ace1552960e36eae0c7fc 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: 0.4.6-2~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2113 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.2, libinsighttoolkit3.20, libstdc++6 (>= 4.6), nifti2dicom-data (= 0.4.6-2~nd12.10+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom_0.4.6-2~nd12.10+1_amd64.deb Size: 475344 SHA256: 60b72d6b843fc120d5ebcb981b209662165ecec0654104c21d7e1c44a0c01fd8 SHA1: 5aa8b5194a66f5c496d228831c1be330ede953c8 MD5sum: 0a0220a1b504bac0678ad2fcd89cae0b 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: 0.4.6-2~nd12.10+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_0.4.6-2~nd12.10+1_all.deb Size: 615192 SHA256: ab6da8727ee8f29b2dd252e5fb92ac58fd2b518240f620379b0e9aa1b33b07a0 SHA1: 7ef07b816b09cd1ffc39ee5ec94401d50b1b7aaf MD5sum: 6c18403aecececa22aa61312c6c95456 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: nuitka Version: 0.4.7.1+ds-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1959 Depends: neurodebian-popularity-contest, g++-4.8 | g++-4.7 | 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 (>= 2.7.1-0ubuntu2) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.4.7.1+ds-1~nd12.10+1_all.deb Size: 516052 SHA256: 15cfb495fb423976bbfd8ccf4e6bea9f36a06ceb3dc89cb1427ae463aa6aa902 SHA1: 632020d3b935933e62262798dd81f6bf8084adaf MD5sum: ea9b6470eeac33e0bfac0b375532180e 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: 5.8.1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 898 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), dpkg (>= 1.15.4) | install-info Homepage: http://nongnu.org/numdiff/ Priority: extra Section: science Filename: pool/main/n/numdiff/numdiff_5.8.1-1~nd12.10+1_amd64.deb Size: 614662 SHA256: e04c0609e91564e0898ce2230bdcac9648abcab13b32898afb9920a71457d4a5 SHA1: 952c3ef53389859a040791e25f3ac40a6a9a855a MD5sum: abfb5dc01fcf0770b588191d1f6c4c33 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libbiosig1, libc6 (>= 2.14), liboctave1 Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_1.4.1-1~nd12.10+1_amd64.deb Size: 24024 SHA256: 01dcaf9df7d3e590e1f26f18f28a7bc8bf69b7dbc67026f2b771561e36b74c29 SHA1: 5c25e2447d0cc2a7f737b7f2b50a6da08d31cf27 MD5sum: dd827364b36bb9c0c078dd5ab10a559a 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-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.11.20131230.dfsg1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2827 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.14), 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 (>= 1:1.13), libpciaccess0 (>= 0.8.0+git20071002), libstdc++6 (>= 4.1.1), libusb-1.0-0 (>= 2:1.0.9), libx11-6 (>= 2:1.2.99.901), libxfixes3, libxi6 (>= 2:1.2.99.4), libxrandr2 (>= 2:1.2.99.3), libxxf86vm1, psychtoolbox-3-common (= 3.0.11.20131230.dfsg1-1~nd12.10+1), psychtoolbox-3-lib (= 3.0.11.20131230.dfsg1-1~nd12.10+1) Recommends: octave-audio, octave-image, octave-optim, octave-signal, octave-statistics Provides: psychtoolbox, psychtoolbox-3 Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.11.20131230.dfsg1-1~nd12.10+1_amd64.deb Size: 917296 SHA256: 66089b4b85859982cbfb01185b8e1beae5e05657fde5a34717047b161d2b55fc SHA1: 903c7f66b6dc1c13e381bccd96baf002aa08fe6a MD5sum: 2636a73af51eae5bb81fc1177bc98df4 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: opensesame Version: 0.27.4-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 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_0.27.4-2~nd12.10+1_all.deb Size: 25359348 SHA256: a8f655afbee639aaf3da3ec8202075a75f66ab7874a216a9f781004dbc800442 SHA1: 98b2189e951cf648607c5e85982a5d8d0230a06b MD5sum: 9788a7f1338030b731637400e91e769b 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: openwalnut-modules Source: openwalnut Version: 1.3.1+hg5849-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20035 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 (>= 2.14), 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~nd12.10+1_amd64.deb Size: 5948718 SHA256: b56dd196e6c3655366a7779abf4712441fa49b595c762c40074e7abdf81f7f09 SHA1: d1eb397c8ecfd5c6ee5a7732fe9e46e70ca01c0d MD5sum: 61e53082d31e59fa966a3b8c4ab9afd6 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1825 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 (>= 2.14), 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~nd12.10+1) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-qt4_1.3.1+hg5849-1~nd12.10+1_amd64.deb Size: 791416 SHA256: 511b2f5b65d59a595b792105ededd003cceea63d9aa5e770a287f0387328ff85 SHA1: 717b15463782322112089c3ae82389649343dced MD5sum: aa7b01481399c59c0af839389c05ed2d 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: 1.79.00+git16-g30c9343.dfsg-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12186 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_1.79.00+git16-g30c9343.dfsg-1~nd12.10+1_all.deb Size: 8109578 SHA256: 74f50d493ca583eb39f282ce24479faec9ca03531685b5b99db05283223c415e SHA1: 6fb4fb7c0b47bd3052d6d90be2b368d0732d359c MD5sum: 9ed7beb29de8eae469b81ddd455d2183 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: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20131230.dfsg1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49703 Depends: neurodebian-popularity-contest Recommends: subversion Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20131230.dfsg1-1~nd12.10+1_all.deb Size: 19957242 SHA256: c94a02ef195a283b5d6030c7e097b55edb0f2729014bf4356de009a1df9e4710 SHA1: a045b74b510ba4712c96a81ca7d2f18b3b90995d MD5sum: 7b4dafe254c4a4d7a9582498f7e3e132 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: 3.0.11.20131230.dfsg1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2725 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.11.20131230.dfsg1-1~nd12.10+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.11.20131230.dfsg1-1~nd12.10+1_amd64.deb Size: 874698 SHA256: 5a566a7aada80c7a142fcd7802c97ebbf38d827e6f5fcfe75e61217436a56dce SHA1: b73a406e90a243fe4380b9edad15657605e73893 MD5sum: 663f70540e96e7626a51f9f374f7bb4e 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: 3.0.11.20131230.dfsg1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 157 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_3.0.11.20131230.dfsg1-1~nd12.10+1_amd64.deb Size: 66054 SHA256: 80ecf839c1c7d5a77fde80ef4c693f460b4d1b0a1431ae25a608324d70c43291 SHA1: a0c4ce51fff36e52800a271a7f2e09cb27c13826 MD5sum: 7b6e4012a6f5e43d325a214146e9873e 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 206 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 (>= 2.14), 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~nd12.10+1_amd64.deb Size: 55748 SHA256: 49c44ea0def0bb6344d6cbcc8088eed0a879bd4c2a1a7ca8b29314ea63378fed SHA1: 81aa94b1d87208ced723adbda2eea9f5dc7f1baf MD5sum: fd00e444d43d07f0be1ea347d70b717e 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~nd12.10+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~nd12.10+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.1-1~nd12.10+1_all.deb Size: 549134 SHA256: 02dc3313fbccc63980a6663e502845d6006df630a9dcac9163438bbe2ee28fe5 SHA1: bf16c73002c99dd728f618a5c4f6dbe54ccc9ed6 MD5sum: fc9f21a9b990da87150cdbaccae59802 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6811 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~nd12.10+1_all.deb Size: 2250674 SHA256: cd2074565599bfe932b248d9d1f3b6d33c746815828186699b8cb23d26f6a39f SHA1: 6ae3811f66847618b743ad140810e9d82f260fad MD5sum: f5d39f86c6b658c2642f6f0244e9c109 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 141 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), libc6 (>= 2.14), 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~nd12.10+1_amd64.deb Size: 54090 SHA256: 917d5f60ab0a6fb4069f65168233afc41029f20d1aed909060c79aca44bd0eff SHA1: e070d189422c18de3a4969ca6310c9df32cbfb21 MD5sum: e6b891b7d400fd42d7a6b9d3bc640a7a 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-dicom Source: pydicom Version: 0.9.7-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1790 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), 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~nd12.04+1+nd12.10+1_all.deb Size: 419248 SHA256: f3f77a173128d07565fa883296bb7e2e6ac71c2c063503b35dd203af84045cc3 SHA1: 4cb7eb491eb9c51dfffa5af9f9c962df613bc137 MD5sum: 843bc43a1ba02475ab7b61e3ca6ceb07 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2285 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd12.10+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.6.0-1~nd12.10+1_all.deb Size: 1586250 SHA256: c723134e582f1f4134bee2f943f0fc0872417b52a713545f3c56c6d1773040a9 SHA1: 8e1cedd72bb06c083e3fb02acb7fb23b7273522a MD5sum: e70f7eb811f0b7a86b16d11d2c73c364 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: 2.7 Package: python-dipy-doc Source: dipy Version: 0.6.0-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5076 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~nd12.10+1_all.deb Size: 3616776 SHA256: b5dcd26b7fd68ea10da38674d2717cb1c6bcc8fb045bc69e79a0b4afed809650 SHA1: 42ab9896a04587cae1cd4d097761f8ce0a7f2941 MD5sum: 30d8cb43d8551e72308eaf21c0732fec 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 942 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), libc6 (>= 2.14) Provides: 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~nd12.10+1_amd64.deb Size: 373170 SHA256: d9692534519463057370835f84caaa5c84d813a6a9e20c33bdde5c7e466a1150 SHA1: 9eba26b5993a9c6a1c1e3b8782e13e6b0cd5a242 MD5sum: 4f7de113b82a8dd35e5c0a2e548fbf8a 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: 2.7 Package: python-joblib Source: joblib Version: 0.7.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 182 Depends: neurodebian-popularity-contest, python (>= 2.6), 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_0.7.1-1~nd12.10+1_all.deb Size: 54854 SHA256: bff06577e8e7f9293f8769f77d2262912aab3ec1dccb5da42355d3d2f7c3128e SHA1: 51cd0fc3df9ca14d96154aa2320160567fe0b895 MD5sum: a3fb5f177163d5cdbc2d6bc27e6b011b 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-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1495 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), 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~nd12.10+1_all.deb Size: 478636 SHA256: b207ab09eba4efd4f211c30dfcad14fd1d186545f49161e0e577ae0070383bf6 SHA1: 12d82087d31fb3448cfa153cc7f6ca57e64e7272 MD5sum: 528bbc072c4a59d025f381b676c3c6f8 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-mne Version: 0.7-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6206 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.7-1~nd12.10+1_all.deb Size: 4052756 SHA256: 5d85e278992fd7debe3e07f22154d01644cb095273775e0a0c2429eae1af8f3a SHA1: 0e8991f35c222622820f0cb95900458223e44a8c MD5sum: e56cc8d24720cf0232c5348c6f03d72c Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py Source: mpi4py Version: 1.3.1+hg20131106-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1358 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libopenmpi1.3, python2.7, python (>= 2.7.1-0ubuntu2), 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_1.3.1+hg20131106-1~nd12.10+1_amd64.deb Size: 451090 SHA256: d519b82057ba1757a59e7dc060db087274c8c6f5e5a712b38ae968ee4793fa85 SHA1: ad203c80ae9b4d21f468f1c72a1f1bd3fdf76842 MD5sum: 27c39919aa5ddf041c6436cf9b9d2862 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: 1.3.1+hg20131106-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5475 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.3.1+hg20131106-1~nd12.10+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python-mpi4py-dbg_1.3.1+hg20131106-1~nd12.10+1_amd64.deb Size: 1297410 SHA256: 347efd203d43277c9989286fccc116255dceb0fea4ff5a5339dabe4c165cbba6 SHA1: 9a9f5675ed849649ddf471da5724496d53bcdef9 MD5sum: cbd9754e7f77106c641fb65c926e3e49 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: 1.3.1+hg20131106-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 256 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd12.10+1_all.deb Size: 73324 SHA256: 1ed10f44ad496322cee2dea2c7886996090d6363fcf360ade19a0c43443a4e63 SHA1: b01291971feb3ab05bbb75c059aaaee1fd05c7b7 MD5sum: 20373b6dc3dc6152c037855faf9cf83a 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-mvpa2 Source: pymvpa2 Version: 2.2.0-4~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4226 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-4~nd12.10+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, python-pprocess Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.2.0-4~nd12.10+1_all.deb Size: 2394624 SHA256: 88db4ff337527e97cdd5308cf6922a5f13acc890912b194e8f37028be0fc88b4 SHA1: 6bcb35bd46cecb83f6eb074ede66b04be807c823 MD5sum: 0f4b6c9615af0fdfa650d9b34d4e04bc 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: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.2.0-4~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19865 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_2.2.0-4~nd12.10+1_all.deb Size: 5310740 SHA256: 04568b6a13449b364d9269f9a3ca57b6321803050990ef79de36fe71a5fa614c SHA1: fcb9e3d04fc76413d4e744715f7ab2162c657e91 MD5sum: 3c3315cb5010d578fdfa9b128c74ee4d 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: 2.2.0-4~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 112 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.7-mvpa2-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2-lib_2.2.0-4~nd12.10+1_amd64.deb Size: 49876 SHA256: 89c3801548b031fa6825f15a108dc6887413e304a227e3948cedc555e56b2a69 SHA1: 58d816c545802778d8e894592f43fefdbafade01 MD5sum: fb64bb0e9a848e30207bbb0b0683a17b 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: 2.7 Package: python-neo Source: neo Version: 0.3.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2484 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), 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_0.3.1-1~nd12.10+1_all.deb Size: 1445252 SHA256: 139a347c68710835cc5a6c2370a394426c2522f7d0355543b0298e97258a0dec SHA1: 93247c364a3d27931f8c5f5dbc1dbcad9b3a6d76 MD5sum: 1b58d0736dd57cde155d701ca0f3df6a 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-neurosynth Source: neurosynth Version: 0.3-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), 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~nd12.10+1_all.deb Size: 32522 SHA256: e80820c4671a0a34814a7a8143fa543947f8703740d507bed2ca3ba3f6c6a7c4 SHA1: bacb76a1b901d346fb1ffa550743fff2822b5de1 MD5sum: c4a14f27e10461cadd2b5657a41dd841 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~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4152 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd12.04+1+nd12.10+1_all.deb Size: 1816340 SHA256: f4393634a41ed4334f5833115835ac674f8fd2f0aaac8a8acecaed5d841b37f2 SHA1: 6e07b237d683b17e0807de6d3faaf078698e2968 MD5sum: 3ec51142db5c228429cb67563faa3222 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: 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2440 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~nd12.04+1+nd12.10+1_all.deb Size: 444170 SHA256: 01df549d5c4ea10fc4712a4ee44ba0d4f6eb3ac668043365cd5a1063bcfc7bbf SHA1: 229ecd36ecd5903eafc04e3553ba1609d861e9c1 MD5sum: 84cbf46b773013a504beb30c532bd5b4 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-nipy Source: nipy Version: 0.3.0-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2863 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0-1~nd12.10+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0-1~nd12.10+1_all.deb Size: 784438 SHA256: 8b25c5d69e46df75985d7370a060691b561963322e0cf3d2cf6b850e5edb030a SHA1: e8544bc3a6b4b6f522387ae82b721b28ccfd5a23 MD5sum: b2c65115481509f8c89a3c4b45ef20d9 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: 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10231 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~nd12.10+1_all.deb Size: 3854166 SHA256: e28a2e5f24771f31983880ea2539b2d56b7343e39747f7402e4e8befc9b92ebe SHA1: 0c7fee0614a9f70b7feebf6bf8877cb6384b589b MD5sum: 5903a71ed70da87e6f5decd1c0e0b141 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1378 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.14), liblapack3 | liblapack.so.3 | libatlas3-base, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: 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~nd12.10+1_amd64.deb Size: 532666 SHA256: 2a40b374d4e83d46b97000254d8620282e2577a29257a8871b0d3005677ac891 SHA1: db179490e619591fc37f61eec7236995571eb98f MD5sum: 85b1acb309b4f7b4666eaf4c291163fc 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: 2.7 Package: python-nipy-lib-dbg Source: nipy Version: 0.3.0-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2188 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.14), liblapack3 | liblapack.so.3 | libatlas3-base, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), python-nipy-lib (= 0.3.0-1~nd12.10+1) Provides: 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~nd12.10+1_amd64.deb Size: 693914 SHA256: 11d703da0674db4036347e1fb3dd9ed0de847b65759a10ad20e1adddf7ddc7b3 SHA1: a43b42d03a2af14cf6ca4897f0d05c3d97ff7b36 MD5sum: 1111117ec1534578154a049fb7458e68 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: 2.7 Package: python-nipype Source: nipype Version: 0.9.1+git9-g8b1d21c-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3515 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: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.9.1+git9-g8b1d21c-1~nd12.10+1_all.deb Size: 768838 SHA256: 5f52fc72d6e9940f69edaa61615a095cf205467fa2a43d70b7382814f7242eac SHA1: bf4e4ba2be6a43df7fc1b24defaa91878a874fd8 MD5sum: 746de71379b701d829b3005056e203af 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: 0.9.1+git9-g8b1d21c-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16572 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_0.9.1+git9-g8b1d21c-1~nd12.10+1_all.deb Size: 7639788 SHA256: a0183519267fbbc08e3002a84eba5df2eb4a33c24f01d571bbab46de686bfbdc SHA1: c443a3f16e0ee3d992abcc62b5ba9cc92a445244 MD5sum: 62247c76031049ec29867b8c7e50aed0 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-numpydoc Source: numpydoc Version: 0.4-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 118 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), 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~nd12.10+1_all.deb Size: 30412 SHA256: 69286113b5a55d69cb678348c5def36e411ca1d3fcccc40147772c0dc3850fb7 SHA1: 783da78413d2584dbd9508a2798439297a582ab8 MD5sum: 59fa2ab64f55e586a1158e987b3fccbe 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-openpyxl Source: openpyxl Version: 1.7.0+ds1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 452 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose, python-pil, python-imaging Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.7.0+ds1-1~nd12.10+1_all.deb Size: 91944 SHA256: aac5edf5e1da27719c1ea0f966d707d7f30e23e185275a96206477e4572f6905 SHA1: 6f3b5e945cf128a2e4eb980867d81c1f3d642763 MD5sum: 8752f99e2660973f4ab657fbd3eb5406 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5629 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.12.0-1~nd12.10+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~nd12.10+1_all.deb Size: 1080656 SHA256: 9b6abf10da5354542068339329799d6cb38cd3a3019d23a436e0799069568590 SHA1: 2e831d7522e5b36a571c426b93a5fd9b1ab2cca5 MD5sum: 796ff625c9dc4281cbb6e6f0f1de3f03 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4119 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python-numpy (>= 1:1.6.1), python-numpy-abi9, python2.7, 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~nd12.10+1_amd64.deb Size: 1510438 SHA256: b036c640dc73ce2d09db2ca36d90e155aeb66789993ad4d9d25cbd7ab7409ec5 SHA1: 6509bd92255f7ffb2f762b5fe9bd1a578df422f1 MD5sum: af25c046aa4c222a87cd3abd346f973a 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: 0.2.1-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 542 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), 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_0.2.1-2~nd12.10+1_all.deb Size: 141568 SHA256: 5bca35f73298245448dcb82952b274ea7e927dcc63aa009cf9f95368bcafed2b SHA1: 9ed716d8627550a4585b8b3cc70ebdeb1063c69c MD5sum: de486187c148c1514db9f55207929889 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: 0.2.1-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 827 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_0.2.1-2~nd12.10+1_all.deb Size: 275022 SHA256: d295e8264406f14d3173ecaa61440b0fd0795109f836ac333a33854d6895175b SHA1: a15b8d176cfbaa940394022b8424b1966c12acf8 MD5sum: 77a7c966687f1e7ceb48eb4fd33c89ef 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~nd12.10+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~nd12.10+1_all.deb Size: 34266 SHA256: ef38c6a84e0c4aa56fda6059fd9e1b9915a4786241c4aaa3b59bc7a718f76e48 SHA1: c5aba371df92f863489b7edaf6d3d020ae612157 MD5sum: 7f84a40d07feaa43a5e794c65f09531b 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: 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 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+nd0~nd12.10+1_all.deb Size: 108526 SHA256: ac7668cdb64b47774df9762de24b9aac50627bad6be6a84fdec04dcca4815eed SHA1: f76663f91f47b7c9b293a0bf0f76f505708a7445 MD5sum: f432ca6d15707941a736616d0fe09023 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: 2.7 Package: python-pyepl Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1390 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-pyepl-common (= 1.1.0+git12-g365f8e3-2~nd12.10+1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libc6 (>= 2.14), 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: python2.7-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0+git12-g365f8e3-2~nd12.10+1_amd64.deb Size: 381942 SHA256: 0f736f34993b2b96d7eea749508bc26c1dc28ac569d7534a06d9665a30808048 SHA1: 6adf33b614eedacdd6052f5e99639077a893d71c MD5sum: 608e5a6bde39cb374458045492afd97c 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-g365f8e3-2~nd12.10+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-g365f8e3-2~nd12.10+1_all.deb Size: 818246 SHA256: 0f890f242946179ede870f8f6d471620a72dfbbadd15577833bf809a3c7eac2d SHA1: d31707fdcec00b44e28f6c102153f0770be45572 MD5sum: 7b705e0884f0e7035a0c94606da120a9 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1879 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.14), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3 | libatlas3-base, 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~nd12.10+1_amd64.deb Size: 589206 SHA256: cef680c42f093fa974247a2b30ea76a3fc7f74977f2258986fc836291bb772f4 SHA1: dc26487146620aa553f45d8433d717148293493f MD5sum: 4eceffe6fcd1344a750bd3aefe8da988 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~nd12.10+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~nd12.10+1_all.deb Size: 906858 SHA256: 59074e78f8759a1d2cc3f7798cac2ae5c9991ecf4fd266909ef1252e91bfe6fe SHA1: 0cc0b5fb138b9896e1905ba8f80e97b338fab08b MD5sum: 0073ef238c61a59b8ff50c2f2534d15b 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 763 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~nd12.10+1_all.deb Size: 175772 SHA256: 6aca773230b5cdc305e46692d4c2e7e6472ac253aae49fdf8c3db2505a64ea27 SHA1: 64017820e9f3c2884c9968983267cda08954bc70 MD5sum: d49969ba06ba7428a644f39639464b5f 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10239 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), libc6 (>= 2.14), liblo7 (>= 0.26~repack), libportaudio2 (>= 19+svn20101113), libportmidi0, libsndfile1 (>= 1.0.20), python2.7, 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~nd12.10+1_amd64.deb Size: 4993474 SHA256: c55522f2962ff59f81c37a0a274496f101b9e3761881b29326644f23340d0e67 SHA1: a6838abe947522be6f1a91493ca0394a1414b2de MD5sum: c29050c22835a1e2dae6badf4d61d3fa 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~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1595 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6), python (<< 2.8), python (>= 2.7), 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~nd12.04+1+nd12.10+1_amd64.deb Size: 445790 SHA256: ec9f28c7206d7fd3c08dbbc6ab39ac9b8daa31c7c6f7d14e3700f15c7aefbce3 SHA1: def333aee110ded32ce8ed68d1b88b54066735af MD5sum: 40eadd4291a5654aa1d471cddbb39378 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 862 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~nd12.10+1_all.deb Size: 190338 SHA256: 08b91ebab764e01025c72071be3e9888c3ce9e07099aade433105d3f5a37ed2d SHA1: d441abc798fb076797de9779a6875840cac0347e MD5sum: 39e65e49f17a5ff4237c5009b77bd45b 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-scikits-learn Source: scikit-learn Version: 0.14.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 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_0.14.1-1~nd12.10+1_all.deb Size: 33362 SHA256: 1c33d39d40a535ac3d19f592442bd45f719ada613d00b3818256b08204eef2f2 SHA1: 30d9cdc33a765ae1c4a87943d55ed340c69b1330 MD5sum: d499235f6906c4cf140cfa5f067bac85 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-scikits.statsmodels Source: statsmodels Version: 0.5.0-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.5.0-1~nd12.10+1_all.deb Size: 5656 SHA256: 9205e5023254b8545235079b3d56871f5b0ef91da1fc8520fccd4090b33d4b6e SHA1: a86da127752e3c7df815e4ab100fd3741693d19c MD5sum: 14c73067037ecb76a8ffe8f9fbf744d3 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-skimage Source: skimage Version: 0.8.2-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4550 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.7, python-scipy (>= 0.10), python-skimage-lib (>= 0.8.2-1~nd12.10+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging, python-qt4 Suggests: python-skimage-doc, python-opencv Provides: python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.8.2-1~nd12.10+1_all.deb Size: 3236934 SHA256: 2f46fa4f6f6038273d18d340913df6a06d1f94c391174f8be3e1acbfe5200d53 SHA1: d46a4e9d7eb6ba175c80cff5fcd1c34bc6903f2d MD5sum: d6eeadd798be650f718e8217dba098e6 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14141 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~nd12.10+1_all.deb Size: 11770002 SHA256: 4026264115ee50d5adb2fb58728a2ac5dacf8633d706b19ab2765c863bb1bbe4 SHA1: 7f17e88420a97503a0e299aa3ce0ff92e8f9eeff MD5sum: e32e1d22e009420ace2fde6cc246af49 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2582 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Recommends: python-skimage Provides: 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~nd12.10+1_amd64.deb Size: 958632 SHA256: 93f9353b642da7c7de3fbced39ec968d7a0dc7132dacc847675d143348d8eede SHA1: ad1ca19b91cdc3e0c7b19d5bd121b0ff8c4447db MD5sum: 41149e01358b7c234ff4df85cbbd78e7 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: 2.7 Package: python-sklearn Source: scikit-learn Version: 0.14.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3549 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-sklearn-lib (>= 0.14.1-1~nd12.10+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: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.14.1-1~nd12.10+1_all.deb Size: 1103664 SHA256: 4274257e77ec37f33dbf8ba4d8d2fc0dafe2f784802a59b5bb1ba99c179ecde6 SHA1: 25495bb28fba89b1bcc609612a7c8adb77bcb8a7 MD5sum: aa0ecceba5b6de7c6a930f39058ed023 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: 0.14.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 579 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_0.14.1-1~nd12.10+1_all.deb Size: 190048 SHA256: 05dd054dea4b97e24f0f5db74d282cc07a8630df29014c64d56eb1cbf7831071 SHA1: 78f828431fc8c00ad2b1f06e4c978571894a21e1 MD5sum: d79e9ccef95233d2b58e8e7f6719f2ae 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: 0.14.1-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3847 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.6.1), python-numpy-abi9, python2.7, python (>= 2.7), python (<< 2.8) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.14.1-1~nd12.10+1_amd64.deb Size: 1517428 SHA256: 2ceb323aad6ab4f3781b20b14cc39a3eb7e9219119e4808440312db7fd0ede72 SHA1: 5556052a43c288e4686eabc9745f5142c4cb88df MD5sum: 1516f657fbcf2ca664c2d689561af31f 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-spyderlib Source: spyder Version: 2.2.5+dfsg-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4009 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), libjs-sphinxdoc (>= 1.0), libjs-jquery, libjs-mathjax, python-qt4 Recommends: ipython-qtconsole, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-numpy, python-psutil (>= 0.3.0), python-rope, python-scipy, python-sphinx Suggests: tortoisehg, gitk Breaks: spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.7-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.2.5+dfsg-1~nd12.10+1_all.deb Size: 1847846 SHA256: 410fc97b1aaf3ce19572d380540abfbfef9a4c3930947076fe64ed238e22a389 SHA1: c03c14e30010e77f54221f88ac9297aa7177b54d MD5sum: 5179ae5b39423d37aab4efae1f01d2fc Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-spykeutils Source: spykeutils Version: 0.4.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2016 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), 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_0.4.1-1~nd12.10+1_all.deb Size: 404360 SHA256: 492e8547515285efde73af86170b8b8b9926d5c9ae7319aa3d1488e50c99326a SHA1: 29e7f576599efc525fec3e990abea768c90dfafe MD5sum: dccb9dfe63ce55b83639275d9138e3d1 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: 0.5.0-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20412 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.5.0-1~nd12.10+1), python-patsy 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: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.5.0-1~nd12.10+1_all.deb Size: 4680756 SHA256: 3d3c7e62c89e3a41211b263a71a3053a54dd140ec30861957737bfb5074cfeb2 SHA1: 138156f37d9f6e4b60db3731efbb00995185239e MD5sum: becd5acf70629466bd5b8ae68393142c 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: 0.5.0-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29887 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_0.5.0-1~nd12.10+1_all.deb Size: 7071498 SHA256: 6a4d778e07bdb750628f3cf166f1475de52a228b0ba5a5a522ae2d75bf34eba8 SHA1: 14f1469aa3433ed8a02dd71f3e3239a9358d5662 MD5sum: 30dcd4ada8f5bd78992b3534fb9d51b0 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-statsmodels-lib Source: statsmodels Version: 0.5.0-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 354 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.6.1), python-numpy-abi9, python2.7, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4) 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_0.5.0-1~nd12.10+1_amd64.deb Size: 104282 SHA256: 0fd3e1b5e105cf27fc783491df7e4dfc452418f0a7786466695f3bb4fa40bf59 SHA1: db19600f81fd476b7de7d145aaa76f4f4aecf191 MD5sum: 03b28fb102f13caa1b57fad2e1fecc34 Description: low-level implementations and bindings for statsmodels This package contains architecture dependent extensions for python-statsmodels. Package: python-stfio Source: stimfit Version: 0.13.5-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 825 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7.1-0ubuntu2), python-numpy (>= 1:1.6.1), python-numpy-abi9, python2.7, libbiosig1, libc6 (>= 2.14), 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_0.13.5-1~nd12.10+1_amd64.deb Size: 273304 SHA256: 78c26f35d79b5aeb58060b476f2974f33e3765661de5a5d37b33971c32c89b0c SHA1: d44b5104e18edb37606e19d51e78b8624ad8d43f MD5sum: 6c9d6ddf4e205fb6fd5bb449c47d93fa 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~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, 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~nd12.04+1+nd12.10+1_all.deb Size: 28082 SHA256: 2a0d8f7bea7b8e7fbe80619282bcd1c0b6874fbc2569c3248451c752f1cdc4dc SHA1: 186db3b9114826618485059e3582945a132d76f5 MD5sum: 7cc577897180b73015b7f99b17c6d04f 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: 2.7 Package: python-traits4 Source: python-traits Version: 4.0.0-1~cbp1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1 Architecture: amd64 Bugs: mailto:bugs@neuro.debian.net Maintainer: NeuroDebian Team Installed-Size: 1675 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0) Suggests: python-traitsui Conflicts: python-traits (>= 4.0~) Homepage: http://pypi.python.org/pypi/traits Priority: optional Section: python Filename: pool/main/p/python-traits/python-traits4_4.0.0-1~cbp1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1_amd64.deb Size: 342538 SHA256: 7e9eb17627c4e095ba287ca65e4b3d7963037df5fbb0387d785ceadb3e4715a3 SHA1: 5532ecf00ec5e61e53d54ced5d2cd281ae38a4e1 MD5sum: 9fb5262187c14bbb30d15b301459d8fb Description: Manifest typing and reactive programming for Python The traits package provides a metaclass with special attributes that are called traits. A trait is a type definition that can be used for normal Python object attributes, giving the attributes some additional characteristics: * Initialization: A trait attribute can have a default value * Validation: A trait attribute is manifestly typed. * Delegation: The value of a trait attribute can be contained in another object * Notification: Setting the value of a trait attribute can fired callbacks * Visualization: With the TraitsUI package, GUIs can be generated automatically from traited objects. Uploaders: Yaroslav Halchenko , Michael Hanke Vcs-Browser: http://git.debian.org/?p=pkg-exppsy/python-traits4.git Vcs-Git: git://git.debian.org/git/pkg-exppsy/python-traits4.git Package: python-tz Version: 2012c-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 114 Depends: neurodebian-popularity-contest, tzdata, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd12.10+1_all.deb Size: 39078 SHA256: e512dd91a25410d50f9579997ac91ad0112084db3472a4d52ecd2bd4294453d9 SHA1: c84153071fa6e5b7565e216b0312f0ce5c7e5806 MD5sum: ac19c9c5c33c317608e638b9a35d9a32 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1738 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgl1-mesa-glx, python-numpy (>= 1:1.6.1), python-numpy-abi9, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://www.visionegg.org Priority: optional Section: python Filename: pool/main/v/visionegg/python-visionegg_1.2.1-1~nd12.10+1_amd64.deb Size: 666434 SHA256: 02c499fcf1a80ffa7c787f89c19dc00ba77ce9a6834b04eda53a029b8eecd42d SHA1: 38663149136531ce4054d54efc9d289b2e2ea9a1 MD5sum: 4d4aa456899e37d4ef340836e6acc81f 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: python3-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1457 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-numpy Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python3-mdp_3.3+git6-g7bbd889-1~nd12.10+1_all.deb Size: 472468 SHA256: 89e6aaa24e0466c077008ab49d9ee6f262e515e0fdc500f760852be9080d763a SHA1: c64c2e32bb873012649d600340e60dee84346879 MD5sum: f82a99a09536a7744d28e6d286e85450 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 3. Package: python3-mpi4py Source: mpi4py Version: 1.3.1+hg20131106-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1309 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_1.3.1+hg20131106-1~nd12.10+1_amd64.deb Size: 441052 SHA256: 0fdc26869c1290a456c9e7d53f12cb630f25e8eca2388ebb13fe1a8f10d6a290 SHA1: 3ec8cfac6739a21c6b6eae4baedeff0070160024 MD5sum: 3911ab9a0ff933351acdcf1d0ffb6ef9 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: 1.3.1+hg20131106-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5433 Depends: neurodebian-popularity-contest, python3-mpi4py (= 1.3.1+hg20131106-1~nd12.10+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python3-mpi4py-dbg_1.3.1+hg20131106-1~nd12.10+1_amd64.deb Size: 1291842 SHA256: 5b6e7fce140b14fde50af3f20c9f4b8c34ff5feb5eb24f18382e65e26c4430f4 SHA1: 86478d6b4018e1163556b3123ca3b7009153cc51 MD5sum: fdd7bcfce4dba504573275f1ed91b0be 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~nd12.10+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~nd12.10+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~nd12.10+1_all.deb Size: 1076248 SHA256: 34aed61e3276913b60194d47dd6049909668f08a083152d3702f570ccda8b990 SHA1: 4faed8807f106e8980619f56096650b73e514c59 MD5sum: 42e1daa6d4523b307480c16fb0440bac 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4043 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd12.10+1_amd64.deb Size: 1482244 SHA256: dc089508c6ee2c03e93c96eb703dd43bd308212798013ccb7d0a2aee4e854c35 SHA1: 1b305e2cc44ffa254344e4d6248b122b40700c7b MD5sum: d76795294b5e686359f5786a41af31cf 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: 0.2.1-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 537 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_0.2.1-2~nd12.10+1_all.deb Size: 140882 SHA256: 790dbc880403be90e950aa27d9ef164637806958ee4c28ad225dd81b9a3821fb SHA1: f3259578b69eea1a6a84d4bee706bfa6a80a5e96 MD5sum: 4d2cbccc19c0671f74389fb9be79a696 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~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 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~nd12.10+1_all.deb Size: 31094 SHA256: 541debafe90874ce85aa69a2e53d7ada2801158b6f51a6d77c5b53e1555133d5 SHA1: 97f75a58b3b9eecf005f3978a69ec811f3d14c1d MD5sum: bc8c7ae5fb1cfdcc84fd641c71adbdbb 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: 0.4.6-2~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3055 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.2, libinsighttoolkit3.20, libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6), libvtk5.8, libvtk5.8-qt4, nifti2dicom (= 0.4.6-2~nd12.10+1), nifti2dicom-data (= 0.4.6-2~nd12.10+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/qnifti2dicom_0.4.6-2~nd12.10+1_amd64.deb Size: 663190 SHA256: b0dce577b208441c38eb94ff3a3153c3989a20a193f28667ca18e72433801240 SHA1: 7cf651ec9430796cef8812dd9c8b96dcb60044d5 MD5sum: 36305502e84a3cdf08fa81ff622e696b 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~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 293 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd12.04+1+nd12.10+1_amd64.deb Size: 178240 SHA256: 44b2d94ca7af2bbe47e723b0d33814f6a9cd042776b3206ec31e2b551b904d7f SHA1: 10f16b182a0f5c8a976d8c965f9631a67837cbf4 MD5sum: 2538e4d9d077d5104d40105c7e6da90e 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: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+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~nd12.10+1_all.deb Size: 10739142 SHA256: 9c07d393b038418f4e4a763e102f3b02018fd0e52aac7356912911b3d92be424 SHA1: 98d45be6ebbf81448758f3b8c5e0420c845db0a4 MD5sum: de77f38be5e04af49e0e01c3cdb186f3 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~nd12.10+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~nd12.10+1_all.deb Size: 52166040 SHA256: 15cf207c9cb8767759256119203b890d3927a35e1386c575d97d7c5e1e050100 SHA1: 67fa3daa1f542fdb0129c6641fd94e4761425684 MD5sum: 05fa87de1ddbc8f05bab311ad33b2645 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~nd12.10+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~nd12.10+1_all.deb Size: 8991192 SHA256: 1b52462aa5d8bdb5add60ea7404d9832ad60fddf0cd83dd4ca5a81bf428ba9bc SHA1: a520b1f65fbcc15468095112f9ad1307f7da1275 MD5sum: bd7595025796fc9c3737b1263a4aa7f3 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: spyder Version: 2.2.5+dfsg-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 94 Depends: neurodebian-popularity-contest, python, python-spyderlib (= 2.2.5+dfsg-1~nd12.10+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd12.10+1_all.deb Size: 36084 SHA256: ff3e12ab0f1c318e3aa0d7c6b21781e2be53be974fe20d29f0f41ead4fe4093e SHA1: a3a380a3668420da2aebde720a6128fa9ff4960d MD5sum: 6ad8a92518cdc075b12dc2f3a8d5997e Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: spykeviewer Version: 0.4.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1124 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), 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_0.4.1-1~nd12.10+1_all.deb Size: 578650 SHA256: 93d3d6425faa6a54a2621dc6730a2c35c3cdef4c3fdc96ea95a2f25d144ed293 SHA1: 3ab946e31432ee5f4e30b63ef424d500439a35e3 MD5sum: 4f606cd8057669b359cf8fc2770233c9 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~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 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~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 28774 SHA256: 49039b7b76aa244e4ab34fb04efe43f167aa10e762799ff318276089bf7c2acf SHA1: f2a5e4c70779898ef2164710d40febc1320a6116 MD5sum: 03a808a4acccdd5a48c6b8d10f8b96e5 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: 2.7 Package: stimfit Version: 0.13.5-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2577 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.14), 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 (>= 2.7.1-0ubuntu2), 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_0.13.5-1~nd12.10+1_amd64.deb Size: 848932 SHA256: b8b5d61f79f00afe73b0818109447ee1a3feb0b02671645df76ba318ec93a2bc SHA1: 9c529d1e7eff3237f25ec2a48572ee40ff1470ad MD5sum: a072fd0151a1f9382e66b6d7593e29a8 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: 0.13.5-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15415 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_0.13.5-1~nd12.10+1_amd64.deb Size: 4475310 SHA256: 7f1ad3f4c4e41f2d953f7a926bf41364dd01c8ee4acb27b363c2880a2f0f2628 SHA1: b93e72ee92e3c00b7c3be97a11c4ae551399997e MD5sum: 7096928ba5525278cafb07c762fee6ad 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: testkraut Version: 0.0.1-1~nd12.10+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~nd12.10+1_all.deb Size: 102648 SHA256: ea9a0dc6202062ce41b66a99c7636103bbfa784f108edb7e3a3a0ca6eee285bb SHA1: a2ebf2e13a47bb725d76a53f880fb5ca9d4e8abd MD5sum: 375ecec2bf2a9be209520da130d2c74e 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: 2.7 Package: tigervnc-common Source: tigervnc Version: 1.2.0+X1.12.4-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 250 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6), libx11-6 (>= 2:1.4.99.1) 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~nd12.10+1_amd64.deb Size: 80782 SHA256: e7047139ae3cf1ecb3fc78bc30304c7dae836a851ac00107c4245bddd8e66963 SHA1: 754bc282ec8921596dc0bd3b5be41ebf92929fb6 MD5sum: 76e83d3d9ed8468bea3df03ef92aca29 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 582 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.6.1-0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6, 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~nd12.10+1_amd64.deb Size: 219128 SHA256: 43215d7d92301c5ea83da314cbb8eae1633da6486b08d454d33229288fd2e533 SHA1: 3b5d0d3cd6e0983121d667a6329d7e7dc1260738 MD5sum: 67811ca903a10b889dc8a55ee3164a2e 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2572 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libaudit0 (>= 1.7.13), libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libgcrypt11 (>= 1.4.5), libgnutls26 (>= 2.12.6.1-0), 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~), 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~nd12.10+1_amd64.deb Size: 1159586 SHA256: 48f5be1103c73ff8a1f5c7eb9a1349a163e2d3958909bc2403edc25905cae9a2 SHA1: 0573159643ca357012b28e1756194f1161f59417 MD5sum: 3c875aaf9a007ac2cc9a701cef2c4f7b 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1105 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), tigervnc-common, libc6 (>= 2.15), libfontconfig1 (>= 2.9.0), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.6.1-0), libjpeg8 (>= 8c), libstdc++6 (>= 4.6), 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~nd12.10+1_amd64.deb Size: 507892 SHA256: 28de407d3f02eee57b58668ecfb5a6a9fc2fd9834e6526fb2252f048b9ae5d3c SHA1: 22048e37de477c1d349c8314a465a80c9b304039 MD5sum: cab8e9e5ed352b755bf399be40ef2029 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~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 748 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.6.1-0), 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~nd12.10+1_amd64.deb Size: 277748 SHA256: c78379d6d23d066f431739125fb5c1aeb7224930b7455519aec9e35871325370 SHA1: e0182a47f941a7b4c0c48deb5f33b9422c493c32 MD5sum: e0b587ec202d7cb5c6bb2d579d7d123e 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: vowpal-wabbit Version: 7.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 51 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libvw0 (= 7.3-1~nd12.10+1) Suggests: vowpal-wabbit-doc Homepage: http://hunch.net/~vw/ Priority: optional Section: science Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit_7.3-1~nd12.10+1_amd64.deb Size: 20612 SHA256: 2ad5a5f08658b1435aacac0330250788d2c2560a0e82f1a5b1183e487f36c7df SHA1: d674cddd1617c0002533e5b8fcfbeffede95ed79 MD5sum: 1f9feeba0532ac0115ec52189cc51016 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: 7.3-1~nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8207 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 7.3-1~nd12.10+1) Homepage: http://hunch.net/~vw/ Priority: extra Section: debug Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-dbg_7.3-1~nd12.10+1_amd64.deb Size: 2398150 SHA256: 17a3967a27bcad352db007df01ad4d4c193170d76513dc67448c5288f3a77028 SHA1: 4ee967f1a306e76f85ea49c3d0e015336f17ac50 MD5sum: 0e34a18def12925bfd05d636df95c037 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: 7.3-1~nd12.10+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_7.3-1~nd12.10+1_all.deb Size: 50202376 SHA256: dc7fd2d40ad8c317ffe38601b1cad124a44d5e1059f649abc1cdaa85da951be7 SHA1: 2a3be7a28b91e82bc980ba1b1091e57c7865607c MD5sum: cf312b9c9a7ed3e5e99368eeddf54aff 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: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 322 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: utils Filename: pool/main/v/vrpn/vrpn_07.30+dfsg-1~nd12.04+1+nd12.10+1_amd64.deb Size: 66750 SHA256: 7549cd91ada9eb015bb8e708cfd79a7ccda1873facfc2fb701efa8910ed8f9fa SHA1: dfe7527fb24e0d8e5f69763156d6b2b260281dc7 MD5sum: 6255914a610f81cf5ec2916d82f2a382 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~nd12.04+1+nd12.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5745 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1), vrpn (= 07.30+dfsg-1~nd12.04+1+nd12.10+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: debug Filename: pool/main/v/vrpn/vrpn-dbg_07.30+dfsg-1~nd12.04+1+nd12.10+1_amd64.deb Size: 1770122 SHA256: a53315e1d5a528d60638976fbe2665f3576d3fa322322712d92b344bb325a8ce SHA1: ca4ba7a0ec18b968f916982ed0f38d62d603e697 MD5sum: c9284cdac0ca1e91b68a5cd8a612d056 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.