Package: aghermann Version: 0.8.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1548 Depends: neurodebian-popularity-contest, libc6 (>= 2.8), 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, 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_0.8.1-1~nd12.10+1_i386.deb Size: 695218 SHA256: 19fa495860820bce5b8466915a7b898938337deeb41040a3060c055d82f85564 SHA1: 0b1d268372f4fec48441ec8a25f788ecd462f063 MD5sum: 56c178a4ba22e183461600f835a02f0d 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 679 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_1.4.1-1~nd12.10+1_i386.deb Size: 286316 SHA256: 9f6a98e7d4e6c3139b6bc1948b4ef9439b384b4ecb7d289dcd06c15212bc6da4 SHA1: da2e87c1116c5e78214441e588bc49946d685332 MD5sum: 532a3a3173f94407a7f6df77d58ae17e 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: cmtk Version: 2.2.6-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 20813 Depends: neurodebian-popularity-contest, libbz2-1.0, libc6 (>= 2.7), 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_i386.deb Size: 6096132 SHA256: 0e5f9cb0783efecc8e92133f5787d65628022721bfd2f79b18e378c2941d2cbe SHA1: 7d56078d0d6071a9152a28dc8782ec1be37991c3 MD5sum: 22332c146d2d3bc1384e5ceb97709057 Description: Computational Morphometry Toolkit A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O. . The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear regression). Package: cnrun Version: 1.1.13-1~nd12.04+1+nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 428 Depends: neurodebian-popularity-contest, libboost-regex1.49.0 (>= 1.49.0-1), libc6 (>= 2.8), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libreadline6 (>= 6.0), libstdc++6 (>= 4.6), libxml2 (>= 2.6.27) Suggests: gnuplot Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun_1.1.13-1~nd12.04+1+nd12.10+1_i386.deb Size: 175696 SHA256: 2f2e4d23d2e499c21c12a95e630ddf91d073a0308af074a7bb2cdd6aa7095a61 SHA1: 5d41a74c346acd4db35cc96cf143aa0502fe0317 MD5sum: a1285331082b1045fb8e1bfc483f32da 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: 7.8.7~dfsg.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 13126 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.7), libclassad3, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-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_7.8.7~dfsg.1-1~nd12.10+1_i386.deb Size: 4640626 SHA256: fe9b080e6ce4c3571001948ef6231213e22576dbf64f5391f29d43d541a4d277 SHA1: f4a9327d46ada82d40abd1083d0b7d781c3d475e MD5sum: 2101b70703c3c8c7b7643ace75cb9acb 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: 7.8.7~dfsg.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 32178 Depends: neurodebian-popularity-contest, condor (= 7.8.7~dfsg.1-1~nd12.10+1) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: debug Filename: pool/main/c/condor/condor-dbg_7.8.7~dfsg.1-1~nd12.10+1_i386.deb Size: 12089998 SHA256: 428bbe2894ff7b54b9d55e44fad256d6c9b1f974f3f5228f76ba03b5877bb86a SHA1: d5f7b97fc8cf41d88142225763935284c8021217 MD5sum: d66f8b435c4505c011d4676d23f1499a 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: 7.8.7~dfsg.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1542 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: devel Filename: pool/main/c/condor/condor-dev_7.8.7~dfsg.1-1~nd12.10+1_i386.deb Size: 421858 SHA256: 3c703defdb987cbcef0cd9af34d3e4539d7b3f957f413ebd0550a55b01942f18 SHA1: 81d90b58b425b1ed92ef3ade436bd0631e1236b0 MD5sum: ecf2261aa2e9758274346a4fb85cba46 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: 7.8.7~dfsg.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6155 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.7~dfsg.1-1~nd12.10+1_all.deb Size: 1334038 SHA256: d1c7a7ec1393442e66988e8727ebbe6de1f93a4c9f39be2ab5ab47bb17298e4c SHA1: eed61659bbc17630729b0f78d8ca5bdac9f3e907 MD5sum: aebc5e8108bb9c9a35aeca820d307fe4 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 130 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_i386.deb Size: 45400 SHA256: d925e5f0f58fec5b5995be2e6af8c5dc955f38de0b108dcdc967ff399daec58d SHA1: e09dce625981ca2d37631736fb58c124fce4388e MD5sum: 18c4bcff9428a26ca5771be2d7640de9 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 19 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_i386.deb Size: 10680 SHA256: 1daf4b7628b9a891f596245f578486247ae010c01ab8436375a61f6652238d65 SHA1: c69b700de197d91ccce2861e55c32b7184d4015b MD5sum: b68b4cc0bec6d1bcca2870eff1fd31cb 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: 0.8.8-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.8-1~nd12.10+1_all.deb Size: 112574 SHA256: b39e2d62703c7a98c1d93ab02a2471c8218cbfa900f8f0b9737b88ae61bfc992 SHA1: 08d81d3a53315d987b029b367034add6deec0227 MD5sum: 7bdf1426517e7497b5f78341dd127b6b 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: 4.0.0~beta1-1~nd12.04+1+nd12.10+2 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4903 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), libqtassistantclient4 (>= 4.6.3), 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_4.0.0~beta1-1~nd12.04+1+nd12.10+2_i386.deb Size: 1955626 SHA256: 8d58e38abc2d65d4665048982120129ec1cbcd32d53a4df523b1007446b276ff SHA1: 49c19a66d9535b21d43a3d8bdf8a05a26f99c9b3 MD5sum: b3f35c591d2280678eb7e1815560cb36 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: 4.0.0~beta1-1~nd12.04+1+nd12.10+2 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2873 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.0~beta1-1~nd12.04+1+nd12.10+2_all.deb Size: 2346236 SHA256: f1d6b4282ebe9447751ca6410cfcfde6a893b4457a62798d9e45874a250460bc SHA1: 2350c17e9f439befa04456e9d282dc2dba7f7dd9 MD5sum: e9d319e13c8fca561d58935b51db0514 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 344 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_i386.deb Size: 127034 SHA256: fca48df800f9f0fbad6fede9f50b49aad41bfa1b5140eb849f848e75e5165df6 SHA1: 1b7247a839f6230ff9792fe23ec334bfdd6a989d MD5sum: 117f28c36936db7a3715c9f5771dec82 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: 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: 0.13.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4661 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_0.13.1-1~nd12.10+1_all.deb Size: 1285210 SHA256: bb8beddfb1597b99b1b0aca6265c68719008f569a99d81e18219f2cd2d5ea98a SHA1: e71ce397f1fb713cb297b316ce9948f71577866e MD5sum: 9361e87ea7dbd5e412592b53d2d56724 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: 0.13.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16635 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.1-1~nd12.10+1_all.deb Size: 7228930 SHA256: 14c74751cb94b2462905f885288e89cf4af430600896da651d8865ca297b5fbe SHA1: 3d34c6126e47be499d5540f0cd067158588b155e MD5sum: a616c5f9c4161a106885b5a7620e3022 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: 0.13.1-1~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_0.13.1-1~nd12.10+1_all.deb Size: 902 SHA256: c7bf09aa5874168543401a85855f9d59b5acf9c3c51ec0099770e49ac99664d5 SHA1: c7749ec14f5a975f6e4805f92c5ffca53ff87e6c MD5sum: 2ab45d60ab4cb38d49f08d5b70858127 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: 0.13.1-1~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_0.13.1-1~nd12.10+1_all.deb Size: 830 SHA256: 4aec6cf4e61e54d4ddb30e57e954ca30a92f9deb606f91a3b418a6cdcf1a7ecc SHA1: 718cf15c2905e382b6a8481f27546abf92e134e7 MD5sum: 82891260957b84a748756fff9b269db9 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: 0.13.1-1~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_0.13.1-1~nd12.10+1_all.deb Size: 912 SHA256: b00958790a164c47d96a48132d70a6a45e05f08fa254ba56408a5b13c999f16e SHA1: 1f9cc05a04a4f641b1ee5688a8562c877d5e7ede MD5sum: bb17a59fe310116281eade9dac09a12d 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: libbiosig-dev Source: biosig4c++ Version: 1.4.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1338 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_i386.deb Size: 419056 SHA256: 4a594f46b2e1376319058d37b00b6d614bab1a66512d2ddc2c4b8c2fd5433bb3 SHA1: b5ee0a3107e5d56258b89bc800ba020ef036b390 MD5sum: 9170c3017429ef38924acb116c10c41a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 811 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_i386.deb Size: 328976 SHA256: a0edd8007f901da78df0d03ed7016040c654c898791ef7a694370ca39bd05001 SHA1: 0b028a2fbbcb4eb3ef809a629b308fdddd22d094 MD5sum: c61a70e532a704be7d108707f0546a5b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 323 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_i386.deb Size: 116966 SHA256: 316b974337afe26876c7580f0a3c822d217b9d0b5ed4df8f2256da6ac6dc06d4 SHA1: dcbab6a3c0523ae243b25b44854271bc9c024ebc MD5sum: a0eea0341a72bf60166db18bc918cc0a 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: 7.8.7~dfsg.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2162 Depends: neurodebian-popularity-contest, libclassad3 (= 7.8.7~dfsg.1-1~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_7.8.7~dfsg.1-1~nd12.10+1_i386.deb Size: 484058 SHA256: 0b0ffb05d7df80208dc112053241b22f502075e67131593a65dbf7637b0abc29 SHA1: 8d0c7771cb3da4685b9223b03df5778b7c4f42ba MD5sum: 55a74119e4e59dfb0c824ca34c6fb7ba 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: 7.8.7~dfsg.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 800 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_7.8.7~dfsg.1-1~nd12.10+1_i386.deb Size: 272658 SHA256: 7eafcc4fe76eb5fd676bb4797cf1c0bcd8e638028916fe83117c872b17077e0e SHA1: c64648b2fd4d6ad42f68392735e446d9212fda9f MD5sum: 324a01de27743cac8542d9e9976edee7 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 523 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 157888 SHA256: 953f7e0dfc2c225dfb9e5cf1509fa55f0b77a04e56c88d2b1634306ff8fafca7 SHA1: 05e2dadd02ac6103f6b1a21f3086026969a66aa2 MD5sum: 181acdb13fb50cabb3324b77a7125c3e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 127 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_i386.deb Size: 40078 SHA256: bc46c94980fdcb625c8d926ad22ef33e59b882b5bfa6f478160e0b82763772fb SHA1: 6d0d7c4efbad874f6e308fa120a60b44b37f51c3 MD5sum: 02fbbfb3bd4597f9b47417544a20af99 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: i386 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_i386.deb Size: 153204 SHA256: a47b4a278be50061686ed6cf6ddd044fe2bdbe7ebfcb6c2659232af249bd31cd SHA1: c0a5ecd26ddaed533f71df4f6eb9154c97527fa1 MD5sum: 6b40a11bf6f76f3d58e34ba48301cc1c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 467 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 141958 SHA256: 96098113a656c02f0782347954227c6c6b32bed67705b3a7c0b1f43fc45e10a3 SHA1: 4b6e435f9df5cd19a046ace555b6d8e1730474a5 MD5sum: 201dcea2391dba7dd93907acd8491984 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 104 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_i386.deb Size: 32368 SHA256: 8c259f4b1e5b0035451360eaf3f83fd03819c056b355de101eeb6b0d74bff280 SHA1: e2e6452ef182b6c6aec1046f08b9718ca706c25b MD5sum: d5a4a77814fe0738412c5736671f3e19 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: i386 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_i386.deb Size: 8816 SHA256: 4b1b2cef102d5a44cd4fd74a0d97947b094fd03156d9c88be9074131c00817d7 SHA1: 1f8ae2c6a1aa7a91794f702c19dee1d78a67d53b MD5sum: e800b9355fa20e1adc5aae0b60d73a04 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: libinsighttoolkit4-dev Source: insighttoolkit4 Version: 4.2.1-2~nd12.10+1 Architecture: i386 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_i386.deb Size: 5275640 SHA256: 176dc2629e6cd5b6e7aa7e1ea48ff956b74ac06c1ac409fb4f52e05e28a4c3ce SHA1: 6fd8c132105597e0185265e0300c08ad4d1d0eec MD5sum: 9308e2ea658af8e765f18ed6d559b63e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 20377 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_i386.deb Size: 6827622 SHA256: 597d9684a5647febfdbec8f3b88621720510e513b0c96730d0c8c2bee422074f SHA1: 3f34c33f96acb2696cfc367e737239f839a49f62 MD5sum: 7d907864a268583263bb41f8842b9396 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: i386 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_i386.deb Size: 2410 SHA256: e80e03a9ba09142f3e0e3c89beb121a35038ac03dc10cd941009dd80a9ba7b73 SHA1: 06e74477d998cc2830a187424fd51e6d9515e0a5 MD5sum: ec5e66e77a485fe8c0390c6b039a4a47 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 135 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_i386.deb Size: 48848 SHA256: 8914b0d5f4a3198b317cd62e28078a2c04d2f6aaa7a60eeaef5dab9ce5537fa7 SHA1: 0880f21d0af62b0fc98eef2838db10eacb5c2bac MD5sum: 1125ab4d00d94b29a717e7275fbbc220 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 16 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_i386.deb Size: 6614 SHA256: 3eb2574d471d8f58c93ccce16ae047709eb444f30e83341d09395dd3be9be760 SHA1: f77dee35467bc432f459801b957f051c404326f5 MD5sum: c7449a4adabe2e3ddea80e6a2b30c8c1 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6154 Depends: neurodebian-popularity-contest, ttf-liberation (>= 1.0.0), libboost-date-time1.49.0 (>= 1.49.0-1), libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-signals1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libopenscenegraph80, libopenthreads14, 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_i386.deb Size: 1764394 SHA256: 431df58e9724aab297df204774c63ff466153346d1f37ce8a7c8da2fb3e40cb5 SHA1: 68218f27b3f49a75325ed351ddbaa7c8eb207914 MD5sum: 7faa45fd2251e80d7c5d7fb82995030f 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: i386 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_i386.deb Size: 304148 SHA256: 0fed6d27f1998d17cf27c0a330ee7393573ed45789af138aae6b37d96f688738 SHA1: 21166c8cbd6a89ab6f0430f195d8fae7f5082c25 MD5sum: f57cd17dd007b38ea59381d014665bfa 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: i386 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_i386.deb Size: 174136 SHA256: 88f1c52922d593ccb7c752d71b236c10eea8f7ed729485a965155c41957ca47a SHA1: 2b84aff1974ff9c15f8d8cabbe9239afe6b75358 MD5sum: 111af6b6ed4954b4bb63c3598a4d44f6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 510 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_i386.deb Size: 184582 SHA256: 8af5c055aac4b43bc46bf9a0d3eb8b6f77d11978ed2229c062937c2c9b005232 SHA1: 0b73057f26307adae6d1cea45594a77eebd5c43d MD5sum: d6aae564e391f07258a66b50daeedc0b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1214 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_i386.deb Size: 425924 SHA256: 40034718348e24e1873fcb31405b01bd2073cb3a2b67f9a8126fe51b70190445 SHA1: cdcee92875c99cd852c6ed8b33ae3b6926804241 MD5sum: 8d319003baf5d02552a2a5c6f9714533 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: 7.2-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1855 Depends: neurodebian-popularity-contest, libvw0 (= 7.2-1~nd12.10+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_7.2-1~nd12.10+1_i386.deb Size: 529214 SHA256: 6d69c9fda95761915d2c0e7caa37c8779fb57962aec0b57b8e18f3da5dba7bd2 SHA1: e868232f5fcb4ca79b7613fda543f393a2c991a3 MD5sum: 4e53994b116df1999be321033b60e363 Description: fast and scalable online machine learning algorithm - development files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains development files needed to compile and link programs which use vowpal-wabbit's libraries. Package: libvw0 Source: vowpal-wabbit Version: 7.2-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 684 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_7.2-1~nd12.10+1_i386.deb Size: 293508 SHA256: 6530d46a0cf523277d13e6ac0f6ba463ee4cf5e0be027f3c34bc03059a25dfc7 SHA1: 5a2ccbf36444dde4b07f204996d0c6c3b4a18b73 MD5sum: 614856b0aec8a9f7380e7fc415849396 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: 2.0.250-1+nd1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2727 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.12.1), libwxgtk2.8-0 (>= 2.8.12.1) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.250-1+nd1~nd12.10+1_i386.deb Size: 892804 SHA256: 3b4c558482043275549e863132fa62b906d81c8ae464628ae1120e1a703ee035 SHA1: e75f9e0298d116c9f5f951d0abeabca9dcfa6bff MD5sum: 4dd14471a4518bb2beea3bf7bca7ba2c Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5, SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 14995 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.3.6-6~), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libpango1.0-0 (>= 1.14.0), libx11-6, mricron-data Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1_i386.deb Size: 5785810 SHA256: 7c0adc47b7f61853304ac5e15bb1f9def5e9de0790dc4cbaf7f3e774666e6da3 SHA1: cf0d81212fb2fb5aae253c10d4f9ce74d5938981 MD5sum: 741b5a649ca94f35f58bd4e6b9e4e3a6 Description: magnetic resonance image conversion, viewing and analysis This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1678 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1_all.deb Size: 1664034 SHA256: 67d11d7a26ee669ce218dec8dedd8de442fcc302031ec0801be4f0a36eaf5428 SHA1: 92e4e8dc8cb2c36ee9f5b889b7596f48d8daadf5 MD5sum: 8fa66eab66da9d13c81f4c66008b472a Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 979 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1_all.deb Size: 735764 SHA256: adf029ff7c6e162ead06e7cafca311ac7780e43b95dcb97d3f4d5000ab6d4f3e SHA1: ec103ce1298820abe5e9962ed4d42e07dc7cd8d5 MD5sum: f4300f300106bdebf22685ddda9e4078 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: neurodebian-desktop Source: neurodebian Version: 0.30~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_0.30~nd12.10+1_all.deb Size: 115088 SHA256: 0b30dd0ba237ab9d7077936e057c1656b5b447f89604abfc9fec57cd15e95a3e SHA1: 40457acfcc62c8e84a2b81d2f60c61984ea9b049 MD5sum: f0f70f88c85c699054dc76edf68b4fc4 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: 0.30~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5751 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_0.30~nd12.10+1_all.deb Size: 5348082 SHA256: 3bde66ac93725e85a731da115274bd38d185bb242c93b8e4241d62ae34979879 SHA1: 28f5db953db6c5ab8934cfaad68fdceed047b862 MD5sum: c0c63a450e695c337e308c518a78a454 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: 0.30~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_0.30~nd12.10+1_all.deb Size: 14930 SHA256: e36233cbc7cba4c8a5d78670421abfc7f5d3502ae8e57ea6728eee08ca27c3c5 SHA1: 150327c9f69d2b42b2189e6348fe167cf429a539 MD5sum: ea0b41723c6c9cd101c01b5fbb9a136d 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: 0.30~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.30~nd12.10+1_all.deb Size: 7260 SHA256: 87d055c34d21dbfba161fdaf701a6db7f1db0a8d196180d3a0285f895fec3a59 SHA1: 7b4fdb1f3926cd074c66d117e3f354dfe1dd303c MD5sum: 1592c4866b39747d89ad5bbcfcd0643c 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: 0.30~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_0.30~nd12.10+1_all.deb Size: 6422 SHA256: 363e24b38af07e5dae112e598d597b4952536bb510a05d952e26d5b49d995168 SHA1: 9ccda8ce0ceb7dba5134359c6c59fbdf13e3c5ae MD5sum: 7b1d443fedc83a331a4cfef669e0141d 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: 0.4.5-1~nd12.04+1+nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2063 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdcm2.2, libinsighttoolkit3.20, libstdc++6 (>= 4.6), nifti2dicom-data (= 0.4.5-1~nd12.04+1+nd12.10+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom_0.4.5-1~nd12.04+1+nd12.10+1_i386.deb Size: 468774 SHA256: e19ca897f08d23566dc3d861831bfa547121696c6e957af782206579853a48f2 SHA1: bb91cc4f567886e7f12a55b16f283f4473bca068 MD5sum: e4ce5403a7d8f063513b0529c97fbee2 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: 0.4.5-1~nd12.04+1+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_0.4.5-1~nd12.04+1+nd12.10+1_all.deb Size: 615000 SHA256: 272ed3d474a443b383fc5f818890d71d703a6860d58ab6fad247607126fe0442 SHA1: a1e46e31f600d80a861cc6fe5af6a78182b356e6 MD5sum: 61cff1026ec14a6dca81be2aef8d6fe3 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: 0.3.25+ds-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1377 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | 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) Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.3.25+ds-1~nd12.10+1_all.deb Size: 349442 SHA256: b4323504302b965a3d1a75bc4559bd887afe9cee706212233a4811a4899abca6 SHA1: cfa3e917a94f1194fcb9bb5b1730a0ef3357f06e MD5sum: bbfbbc29b39c82d5a194604c9c2240de 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 to pure Python objects at all. Package: octave-biosig Source: biosig4c++ Version: 1.4.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 66 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libbiosig1, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liboctave1 Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_1.4.1-1~nd12.10+1_i386.deb Size: 23502 SHA256: af3f34a4df6c377beb8a5ee8b3ab3be121df971c516eb77eeadbb9dbfbe6052b SHA1: ee1195a8ff4cc9d1d9837bfc884e83cfb8c3dfdc MD5sum: cf256be89469654a413f1bb7aec3497e 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: 3.0.10.20130114.dfsg1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2626 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.7), libdc1394-22, libfreenect0.1 (>= 1:0.1.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglew1.9 (>= 1.9.0), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.24), liboctave1, libopenal1 (>= 1:1.13), libpciaccess0 (>= 0.8.0+git20071002), libstdc++6 (>= 4.1.1), libusb-1.0-0 (>= 2:1.0.8), libx11-6 (>= 2:1.2.99.901), libxfixes3, libxi6 (>= 2:1.2.99.4), libxrandr2 (>= 2:1.2.99.3), libxxf86vm1, psychtoolbox-3-common (= 3.0.10.20130114.dfsg1-1~nd12.10+1), psychtoolbox-3-lib (= 3.0.10.20130114.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_3.0.10.20130114.dfsg1-1~nd12.10+1_i386.deb Size: 885890 SHA256: 07f5c015ba45e10034a06f3b4efbb5defe4d67ab5e819bf69992822421524c74 SHA1: d770861db5eb6e46b6a78bbef97ee131fbeedde5 MD5sum: 3263a5dc3b8d53ea9d2dd749c5de4a77 Description: toolbox for vision research -- Octave bindings Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains bindings for Octave. Package: opensesame Version: 0.27.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25107 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) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.1-1~nd12.10+1_all.deb Size: 24041772 SHA256: c6f501439c4c987ea6e76068bee816635439692352d463a123c63ee387a62e46 SHA1: 3f22f0b6cdd5073a12f7017f46535f312c488015 MD5sum: a10ef2f226e9ad205cf218f5700d283f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 18218 Depends: neurodebian-popularity-contest, libbiosig1, libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-signals1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libopenscenegraph80, libopenthreads14, 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_i386.deb Size: 5942206 SHA256: d957e8adb9caf2b55787ebe4d7ba7670e2b37747e75efc93ed3b53f05e30f05f SHA1: be916314c16f6eb63002d724253b35b23148db2c MD5sum: 44de401b436a36ac64fae3c355943944 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1775 Depends: neurodebian-popularity-contest, libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-program-options1.49.0 (>= 1.49.0-1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libopenscenegraph80, libopenthreads14, 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_i386.deb Size: 796298 SHA256: 3274a1f5c42bb41b1b09d30ea20d9d4a8f1860b7009eae2a2675395a0cfeb212 SHA1: 9ee6a8840a50123d38c0bee26fd227a96e92ad2b MD5sum: 11a6718fb71565df0feab6b8d06916ba 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: psychopy Version: 1.76.00.dfsg-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5340 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, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.76.00.dfsg-1~nd12.10+1_all.deb Size: 3177708 SHA256: bf697c91e97fd97e238a844aba82bcea5becfcdae0429b2ed13129097b6ea34d SHA1: de75ee849bf0cd52e7f0df06fb0e437379f0d5bc MD5sum: 2a561b8d62757f4cd73095f797e8bf78 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: 3.0.10.20130114.dfsg1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48860 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.10.20130114.dfsg1-1~nd12.10+1_all.deb Size: 19678518 SHA256: ad57d02b6039af176007cf3efdd2afcbbcd602419d1457f532fb28fe15f06635 SHA1: 0e002699087d8e9a3b5c41f9bd1dc7ca6de369d7 MD5sum: e1319b9bd63cb0ca52399d5fd96178db 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: 3.0.10.20130114.dfsg1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2404 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.10.20130114.dfsg1-1~nd12.10+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.10.20130114.dfsg1-1~nd12.10+1_i386.deb Size: 871874 SHA256: 01f9349a0e7dfec4e340945aab6861b5656dcb6f13851b9d1c67d2ec2f747db0 SHA1: 5ed9eeb2ad41be7ad17e234572f115f8c1154d4a MD5sum: 1dd930ee35b734a21939ee2157eaebc6 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: 3.0.10.20130114.dfsg1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 177 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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 Suggests: gstreamer0.10-plugins-bad, gstreamer0.10-plugins-ugly Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.10.20130114.dfsg1-1~nd12.10+1_i386.deb Size: 101900 SHA256: a38b3f10fbba6a3afb7eaa36c0ac5bbba9df00b1d6e0ee6306d0731feb5aeab8 SHA1: ef583af8dde5d58c7ee34e306260d95bcc514e0d MD5sum: d0a7e5ca73a7d2a64cf6946d5aa481c0 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 190 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 (>= 2.4), 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_i386.deb Size: 55296 SHA256: cf7d2053f8550d95c4b885fbb6e2ab8e2612a6ebf09127de31995cdb4ad3dc1d SHA1: 66179253e6cffedeceeb1027905a55779b07e7bd MD5sum: 8845a0614fcb558619dfe3ed7d93e32f 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: 1.4.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2139 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.0-1~nd12.04+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_1.4.0-1~nd12.04+1+nd12.10+1_all.deb Size: 503240 SHA256: 51357e842efadc5bb97f1842c7b5269fed5e668a6d5d9f89550d2ac2d624ac0a SHA1: d2bb4f2f816f32e4c53b01ce1e336d717daebed8 MD5sum: 18ef5b7b8eb127e34855a44fec92fe1a 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: 1.4.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6133 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_1.4.0-1~nd12.04+1+nd12.10+1_all.deb Size: 2179816 SHA256: c3a02bcfbbe6eff889c509b8b261d4c32759d7c5801ef546b02113cd520cd0dc SHA1: 44477ae2af4a058ea08c193390b55a6198ad990e MD5sum: 63863d69005f9f9ab8cee59d40c71fd8 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: 1.4.0-1~nd12.04+1+nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 144 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 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.4.0-1~nd12.04+1+nd12.10+1_i386.deb Size: 56042 SHA256: 2aafd8bc2572802e0b6f6413b59241e1f9c83840cc32385e3412895aaf1313fb SHA1: 1b0b9e8c98095ea68f26422b9af11fa0b98761c6 MD5sum: b9d2bba19b310178740f80b709339707 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-joblib Source: joblib Version: 0.6.5-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 175 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.6.5-1~nd12.04+1+nd12.10+1_all.deb Size: 52690 SHA256: 7cdd6f6998be06124635d64c1993ec41abc7520c83d71a6de17cd104833a6fdc SHA1: 9f19da28d98df6a76983d00eb64b3b6de2f9f8f6 MD5sum: 408d1245807b0d85afdbb3aa835bfc4b 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-mpi4py Source: mpi4py Version: 1.3+hg20120611-2~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1357 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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+hg20120611-2~nd12.10+1_i386.deb Size: 434332 SHA256: 72de93215304b4a5026d0f3027683f46a7b1a9f0bb6d495ff88558575c98afb5 SHA1: ef6f80e3e48e742e18f443ed1c2213e129a9ccdd MD5sum: 4571b8259e0ec9d2e48fd9f5d5a532d2 Description: bindings of the Message Passing Interface (MPI) standard MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). Package: python-mpi4py-dbg Source: mpi4py Version: 1.3+hg20120611-2~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2814 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.3+hg20120611-2~nd12.10+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python-mpi4py-dbg_1.3+hg20120611-2~nd12.10+1_i386.deb Size: 973542 SHA256: f99165a33103c7f7269b050f9b5849045d617487288db3aa1661cff5ea88eeaf SHA1: b4b043ed55af9ee2d1d7e12ab42a711198e45707 MD5sum: 0b4b20d22ab3df7ec66ed1f867e9856a Description: bindings of the MPI standard -- debug symbols MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides debug symbols. Package: python-mpi4py-doc Source: mpi4py Version: 1.3+hg20120611-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 284 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3+hg20120611-2~nd12.10+1_all.deb Size: 82524 SHA256: a71a3e30acd4ca335666f96ef827fdca039a1873e8a2dd45f579aed595d561ec SHA1: b3b21af55fab7ce1775ba864328d0023b53af1a2 MD5sum: 305217a25234435dd5ae7761c214dee8 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: 2.2.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4241 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-1~nd12.04+1+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 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_2.2.0-1~nd12.04+1+nd12.10+1_all.deb Size: 2399986 SHA256: e77909b0dccaa44717beab4afe9e83c2c86ff46d9197e562ed27fcca4aca0f22 SHA1: f49f7edfd5c1c294464b7c97cd3243748aa118a9 MD5sum: 7240798423edc4a1d318d8aa34a9d3c0 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: 2.2.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17215 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_2.2.0-1~nd12.04+1+nd12.10+1_all.deb Size: 5140662 SHA256: e2632da5ca320dbdd3fab7f532c4fa11990e312979190db16e918ef30dc0e841 SHA1: ba6389d7ed10f3a6bf29eabba44936dbe7bafa6b MD5sum: 505c6dcc506aa9d7bce4a2c8e91bf82c 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: 2.2.0-1~nd12.04+1+nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_2.2.0-1~nd12.04+1+nd12.10+1_i386.deb Size: 48898 SHA256: 5e118e81a137ff2ec70af950fc6a6c9b471d26086a141bf5c69d194eb1ca8622 SHA1: 5e627e4527541bf8cbcf9812a8a71839ae271372 MD5sum: 83d8b4906ea4ff3c1430ed65154df336 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: 0.2.1.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2414 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_0.2.1.1-1~nd12.10+1_all.deb Size: 1431096 SHA256: 0e7507d79ccc6d8da079e279c419571a6439915d6dad89711afac2349170b53f SHA1: 7a5174cf5f57d52865205d02a64f51c3ba3d3e4a MD5sum: f6a8587f1337ee0ceba2e7232f4d70e5 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-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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1311 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.4), 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_i386.deb Size: 495306 SHA256: b1018909407424a27dafc6ec0dcbf4af1160e0984d2d2688c952ff913614977a SHA1: d5cce35bef62f498fe01b25f952c975868c13701 MD5sum: 23daf5561f24f5cfa51e9f2163e33fd1 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1872 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.4), 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_i386.deb Size: 562280 SHA256: 52f7d0a78e7eb3b82f9c5070e92bdc005633771fa34a371dab76628143ee0978 SHA1: e93f10786e7171c2540e8df7ebc2da0e5f4f9f5a MD5sum: c4ac58fd4d1ef40287d59e1d537a10a6 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: 0.7-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2544 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_0.7-1~nd12.10+1_all.deb Size: 567684 SHA256: cf58de89b262a9e5bbf563c6c4bc4fa8aa3581f986b1674c1385f517927a2184 SHA1: f31d6082826e609220fffc11471d890be57f4884 MD5sum: d9f51ffc846d8982c29013bed209d49e 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: 0.7-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14118 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_0.7-1~nd12.10+1_all.deb Size: 6731616 SHA256: fa7363f963ffa2cc595ffb8ed8bd9356485747d9f842d8d104f117b2d4f54bff SHA1: c29f62d974ee3b0daec3bcc75e0dc44e3d404725 MD5sum: b7b7251770773399fd0efdd9d795c7f5 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-openpyxl Source: openpyxl Version: 1.6.1+hg2-g4bff8e3-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 291 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.6.1+hg2-g4bff8e3-1~nd12.10+1_all.deb Size: 62036 SHA256: a47190b1b27e4cf5766f03106d4696742ec1f4b13a45ac1b619072f6f6cd6bda SHA1: c5200590bb1891d43b10a615c268b1cf03012766 MD5sum: 30449e2a38b1e65c3864e18763a7207d 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: 0.10.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4160 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 (>= 0.10.1-1~nd12.10+1) Recommends: python-scipy, python-matplotlib, python-tables, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.10.1-1~nd12.10+1_all.deb Size: 849732 SHA256: 54cc60c715ae93a1512d4a85bb54200090038baf68c301286b5b08b35c9d7f78 SHA1: 229fd1e603b007fc8f6157827198bc8c90dd9b59 MD5sum: 293cc30e3e3f479853ab43e0a4f2bbf8 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: 0.10.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3200 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_0.10.1-1~nd12.10+1_i386.deb Size: 1189200 SHA256: d12c2d434347b76faff32293319c78744f2b5bfb5d02bc7f657274c1e5f66b35 SHA1: 1b94ac2e3e9d7d5dae3410a40a123a3589b6fe32 MD5sum: e587a3fa121834fb276ff485c3f95c7f 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-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-pymc Source: pymc Version: 2.2+ds-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1699 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 (>= 2.11), 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_i386.deb Size: 497458 SHA256: b09bb837e155d897a10060b0df44548d4ba23cde910f98d1d5ec23c4d0ce1741 SHA1: 226f4c9bc0ec13a33e11950955f9a01e0dabae12 MD5sum: a6ef56ef5397d5a19338be5af6c7e61a 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-pypsignifit Source: psignifit3 Version: 3.0~beta.20120611.1-1~nd12.04+1+nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1488 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 425236 SHA256: 0e9584562a3201abb8589ebbc216be71fae4371c1c609c7059b7fcf2d5c3898c SHA1: eaa528a503729edb8e08765a233a645e0ab1d547 MD5sum: 3e453aefceafbe8491f01c1d95263018 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: 0.13-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.13-2~nd12.10+1_all.deb Size: 28138 SHA256: 68389fdd6ad5ea906c0e56b9cf727722d2b6306d5e2fc1c92e3a04e5335d1ff8 SHA1: f8c06f7dc1ca09093cc89a2a6958f13e7be25898 MD5sum: 91531a99630e141da0a32f7866d7113e 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-skimage Source: skimage Version: 0.7.2-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4389 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.7, python-scipy (>= 0.10), python-skimage-lib (>= 0.7.2-1~nd12.10+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging 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_0.7.2-1~nd12.10+1_all.deb Size: 3155780 SHA256: 392df1b4e8e3870bf6768764aea22cf3b7eb6d3a46f8fa2b4ab3265b4c3d4b1e SHA1: d117de4bc8d07eb8423b352ef1ad4977eb937b03 MD5sum: 3bd131f4fff3ab602af73194d1561e1f 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: 0.7.2-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8354 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_0.7.2-1~nd12.10+1_all.deb Size: 6578200 SHA256: a10af35a7f38140767c659a2402ddd97af358a2408b890320bc33ab40c4ed2c2 SHA1: 31e1208f75da7ed477f45682949fc1d938306663 MD5sum: 714339d017e3e05e32d4c702be97fdf0 Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-skimage-lib Source: skimage Version: 0.7.2-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1427 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_0.7.2-1~nd12.10+1_i386.deb Size: 575936 SHA256: a32efaf8f4e723159a7d84c9df0b04e9bb82205fbf39e95b793fed8268a6dff8 SHA1: 3ced2f0b8b4d62bbb215bfb5606310887f37858c MD5sum: 37b41dc109ece47856d71508faefaea1 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: 0.13-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3034 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.13-2~nd12.10+1) Recommends: python-nose, python-matplotlib, python-joblib (>= 0.4.5) 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_0.13-2~nd12.10+1_all.deb Size: 1007446 SHA256: 8723b37dc521e9a2c92346dd6899bd7ff1f61e2d11598cd456d9823728506059 SHA1: 58e221af408877de9b5e5b7f6e490cb8ec7c05c4 MD5sum: dab964ac53460bbaa7632e5f7787108d Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.7 Package: python-sklearn-doc Source: scikit-learn Version: 0.13-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42243 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.13-2~nd12.10+1_all.deb Size: 30776416 SHA256: 7737b9768d6b323c8e380cac3edb88e18ec059fcb838ee4119c4ca5b331b2819 SHA1: 45dcec7e5e1d6cc32c881b26747a54131b81f37a MD5sum: 6969df86d729cac2c273f0c3631fc985 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sklearn-lib Source: scikit-learn Version: 0.13-2~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2350 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.13-2~nd12.10+1_i386.deb Size: 920848 SHA256: e2691b4586ffd88d3a687738375aa5772eb24774e8256c67efe4d62e514af66c SHA1: 16ad1007ed4c6c7341b31dfb71ae9b9e6d830a0d MD5sum: 8e8764180b3ff8b21fb0e81f223c8b3b 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. Python-Version: 2.7 Package: python-spykeutils Source: spykeutils Version: 0.2.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1392 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_0.2.1-1~nd12.10+1_all.deb Size: 324020 SHA256: 7dc79235cb23e644a16c2c2982663c9879e2d3627f9b104a07b892dd5a3b9909 SHA1: f8de6e4621f3ebcea34625dc9c5f8498dcdb6069 MD5sum: 7e05e0a4963588d45abdbdda84f8e74b 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-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: i386 Bugs: mailto:bugs@neuro.debian.net Maintainer: NeuroDebian Team Installed-Size: 1662 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 338408 SHA256: 827aa0b66afcb850fae324eaad560240f1bf0648bd9400bdc562f9cfe9a1abe4 SHA1: 35702275f49497eda3e1a4c9edbdcf5d27b415c6 MD5sum: 9c1944e8a319c7685b2af4cd51a2dc4b 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: 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+hg20120611-2~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1315 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libopenmpi1.3, python3 (>= 3.2.3-3~), python3 (<< 3.3) Recommends: mpi-default-bin Suggests: python3-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python3-mpi4py_1.3+hg20120611-2~nd12.10+1_i386.deb Size: 425890 SHA256: 5c87518a6cba506ee480e7103163e2b3ec0a3a32c8d021e2b8a3b342a0c66224 SHA1: f8599763f260b20005d8f77bc3c2c6f0556cf2b1 MD5sum: 080a7baea5f1b8d819202796991eac7d Description: bindings of the Message Passing Interface (MPI) standard MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). Package: python3-mpi4py-dbg Source: mpi4py Version: 1.3+hg20120611-2~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2787 Depends: neurodebian-popularity-contest, python3-mpi4py (= 1.3+hg20120611-2~nd12.10+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python3-mpi4py-dbg_1.3+hg20120611-2~nd12.10+1_i386.deb Size: 965384 SHA256: 4f20c6c386e30b5f0fc8d0dba7f7447baf9f4f61b623036774fdb6fec4775517 SHA1: 12ea7b3618ca1a2566bb5da39981f9b330fb6280 MD5sum: 799ca5c5400d0bdd2269e822b880da5f 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: 0.10.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4113 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.10.1-1~nd12.10+1) Recommends: python3-scipy, python3-matplotlib, python3-tables Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.10.1-1~nd12.10+1_all.deb Size: 843292 SHA256: e43eac1c95c1803120042d7a53cecf0dcc6970312264bac635e99d453b5c4571 SHA1: f7b998186de048609f890c92e172e9904ddf71a6 MD5sum: 88bff8b5d921d560acfd8c291ba50629 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: 0.10.1-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3099 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_0.10.1-1~nd12.10+1_i386.deb Size: 1163946 SHA256: fb7ba4a005faa764628fe7bfe34b9390159e0be2faed1418d090e0e12fd1c467 SHA1: 1b8c1c2b347ddad661c517e78acc7ad17c0b86b2 MD5sum: bd32466cc7aa83662cd0f07903d76e82 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-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: 0.4.5-1~nd12.04+1+nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2997 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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 (= 0.4.5-1~nd12.04+1+nd12.10+1), nifti2dicom-data (= 0.4.5-1~nd12.04+1+nd12.10+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/qnifti2dicom_0.4.5-1~nd12.04+1+nd12.10+1_i386.deb Size: 656046 SHA256: a721993de21a21f49262870ff9092b6fc00937e750b916401e0a618d7c1c3ea7 SHA1: e7d0c1732e19176034cd666f0eabbde21b7df5cc MD5sum: e5fadd9fe4e63ef3e3d55f4111824ade 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 285 Depends: neurodebian-popularity-contest, libc6 (>= 2.11), libreadline6 (>= 6.0) Homepage: http://bashdb.sourceforge.net/remake Priority: extra Section: devel Filename: pool/main/r/remake/remake_3.82+dbg0.9+dfsg-1~nd12.04+1+nd12.10+1_i386.deb Size: 173770 SHA256: 99627ab405a4fa8e3fa56e6474c8f96ed210bc89823fcd94fdbc94a2d24a5c81 SHA1: 0a2041121a1d3739e2e32bae634e7196347cab8f MD5sum: 0f4b3c9574061d5f3546d325c218605b 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: spykeviewer Version: 0.2.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 847 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 (>= 0.2.1), python-neo (>= 0.2.1), python-matplotlib, python-nose, python-scipy, 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_0.2.1-1~nd12.10+1_all.deb Size: 463176 SHA256: 71495e7b374bfc4b95ffc47714065630564e5f51b5d70874de94ff1ea7b2463a SHA1: 9c9f3bf7d14a5de441a4cfda7e829f1782232321 MD5sum: 24223e808c63658416dc42e896f30128 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: 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: vowpal-wabbit Version: 7.2-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 44 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libvw0 (= 7.2-1~nd12.10+1) Suggests: vowpal-wabbit-doc Homepage: http://hunch.net/~vw/ Priority: optional Section: science Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit_7.2-1~nd12.10+1_i386.deb Size: 20816 SHA256: 08311cbd2fb47a8b2b9199095f4ae31d3f243eca6a8c643afcd294a4f365bb90 SHA1: 9a61aef1e532c3d7026ccd0aebd734361b3e7e78 MD5sum: 268de83614d7a8dcbaa11d6fe259e44d Description: fast and scalable online machine learning algorithm Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing Package: vowpal-wabbit-dbg Source: vowpal-wabbit Version: 7.2-1~nd12.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5476 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 7.2-1~nd12.10+1) Homepage: http://hunch.net/~vw/ Priority: extra Section: debug Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-dbg_7.2-1~nd12.10+1_i386.deb Size: 2118794 SHA256: 47db6e6757cbc2f9563039079ff09deff5283ff690ae6c2a7d827a78967354a5 SHA1: 5a600e455ec8f052c1edf845ce7af84d2ad81e32 MD5sum: 8f8113fd091e98590df331c9f96eec93 Description: fast and scalable online machine learning algorithm - debug files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains debug symbols for the binaries shipped by vowpal-wabbit packages. Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.2-1~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_7.2-1~nd12.10+1_all.deb Size: 50202274 SHA256: 394a2687554b3e8c2dd575d821ceba889487dcffecf3a81db28f6e7b91d7c2dc SHA1: 294f282a3bb80fd4b35ebd8e8bb8a51a7b176182 MD5sum: 0cf2ef4740dbed5a31ea7522941e4962 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 284 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_i386.deb Size: 65510 SHA256: c73c4c0b0a9c2fa9b070baab5f7e84e7109fa880d9b94becc6e2269230052988 SHA1: a5319d5d1e404f41d1ee0649e068127f69f82933 MD5sum: f8c95f12e094ef2730f70aa7141fd796 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4171 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_i386.deb Size: 1596596 SHA256: c02bbed93367563a2798cbc93b91efa765b7e27aeca6cedc060e631f74b68486 SHA1: 204119accbb2ebee9ef96ae847b187bde29ac89d MD5sum: 6f045e74cd24b434a843ebc857e2044d 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.