Package: aghermann Version: 0.9.0.2-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1714 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcairo2 (>= 1.2.4), libconfig++9, libfftw3-double3, 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_0.9.0.2-1~nd13.04+1_amd64.deb Size: 759974 SHA256: f2c43b2894bdd537155624118a71640eea1d36b3c41a120859a7dce5baebbb01 SHA1: ed35ce0b04db912c2da837354e6087748aea3fae MD5sum: 08264c54129bba970d39f37d9fa8587a 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 667 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_1.4.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 282686 SHA256: f142cdd7d5601972ebd7ecbfc0171079f220354f6667a4362e064bd7abdc3ded SHA1: 7c43c7d8b12af57ad5eea540ad169820189e3be8 MD5sum: 69a5f47e32314cb42a083770ab670b80 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: condor Version: 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 14699 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.14), libclassad3, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-common0 (>= 14), libglobus-ftp-control1 (>= 4), libglobus-gass-transfer2 (>= 7), libglobus-gram-client3 (>= 12), libglobus-gsi-credential1 (>= 5), libglobus-gsi-proxy-core0 (>= 6), libglobus-gsi-sysconfig1 (>= 5), libglobus-gss-assist3 (>= 8), libglobus-gssapi-gsi4 (>= 10), libglobus-io3 (>= 9), libglobus-rsl2 (>= 9), libglobus-xio0 (>= 3), libgsoap2, libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.10+dfsg~), libldap-2.4-2 (>= 2.4.7), libpcre3 (>= 8.10), libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.6), libuuid1 (>= 2.16), libvirt0 (>= 0.5.0), python, perl, adduser, libdate-manip-perl Recommends: dmtcp Suggests: coop-computing-tools Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/condor_7.8.7~dfsg.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 4910732 SHA256: ab8c331b13b3afb42ff0241433fbe5be419ccb3692f40a3ef19a0f820b27b399 SHA1: 4f4e282bbbda376346149c5f8d807d7509feb707 MD5sum: 66c9c920fefc1779c136212eb0d0023e 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 35905 Depends: neurodebian-popularity-contest, condor (= 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 12365120 SHA256: dc3d6c25f486aac190b103a831ff83c860d83369fec36ef9a839510b69db378b SHA1: af6507e0f615db9909dda6070e577b21e1a54329 MD5sum: a75113366f76d4f1c7c3ccfe32b0ca6c 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2036 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+nd13.04+1_amd64.deb Size: 460710 SHA256: 4c2fe3e1a0c3bf29f760b235c3a9aab7f34db5f11356643743549872702a1dc2 SHA1: 60bb600bdac0c4541b56b80a52644e33e62ee39b MD5sum: 3ca20f7cfcba53b57e3c1d7ee39c2ab5 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+nd13.04+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+nd13.04+1_all.deb Size: 1334648 SHA256: 475b6c8425b6b73f5b3dd5e19b6b660dbeb77f7ff2f4400c39b8f7589f5d1801 SHA1: 5350154e1e2f98753a2d35d86a759f0ff6a7adfd MD5sum: 8a232078ed3de2b8c189b865d1b0cf78 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 147 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-double3, 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+nd13.04+1_amd64.deb Size: 48128 SHA256: c3ab676f3948c4703b2f38428b1c09dc759c9b09f3cf9c7c020d04f2e442bf96 SHA1: 932ba105ef968b89ce83937b13d42f3cd5ddb294 MD5sum: d46f370e55a6f38d758286b32a904888 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+nd13.04+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+nd13.04+1_all.deb Size: 7224822 SHA256: e03522059ac09830cf48fd4f41780c0e6fcc7c4d1f3c331f213dbc6743c49565 SHA1: d9675743da0b53adc7682ebacfc6a4922f7a0880 MD5sum: 8a6520b56c5bf5302d61f9eb27b6a847 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libeegdev0, libmcpanel0 (>= 0.0), libxdffileio0 (>= 0.0) Homepage: http://cnbi.epfl.ch/software/eegview.html Priority: extra Section: science Filename: pool/main/e/eegview/eegview_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_amd64.deb Size: 11078 SHA256: 8b63c41e24d22ce350e373adb5ba1e34df8c174f3b99d5caf054f0b0514bf353 SHA1: d76df4adc9fcde5cd2fd073563e2cb804b39d606 MD5sum: a08d684fb0a68a6df02c7afdc578c137 Description: Software to display EEG data in realtime This software allows one to display EEG signal in realtime as well as record them. It is the minimal recording panel needed to do simple experiment. Package: fail2ban Version: 0.8.10-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 402 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.10-1~nd13.04+1_all.deb Size: 134664 SHA256: 3cf940095997f8e1ec5ecc7bdae05c24a81d3aca04a89ccbe6e39b9ca5bb2def SHA1: 04b6acf73e5193136964026922090288ebae4a55 MD5sum: 51d94e71407f984949be53685718e559 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Package: fslview Version: 4.0.1-2~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6520 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt4-qt3support (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.7.0~beta1), libqwt5-qt4, libstdc++6 (>= 4.6), libvtk5.8, libvtk5.8-qt4 Recommends: fslview-doc, qt-assistant-compat Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_4.0.1-2~nd13.04+1_amd64.deb Size: 2331890 SHA256: d2743380224c0e5c1aa339cce4a3b3107aaf37b00fc87e813a712bbfe5c4a076 SHA1: 52eade42ef9dd29199a5bfc8469f9c2713ac596f MD5sum: 1e0fbb9e17143418502ffce7de2489bf Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 4.0.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd13.04+1_all.deb Size: 2346538 SHA256: 21191091e9505223e32987c04eb1acbe3829ae341eef5525489f97a1a2d457bf SHA1: 33c8b353b6b9e7b5824704f2f58440ec2d4ad6f0 MD5sum: 66d92ab90f1579986c945c1cf4e2f90f 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~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 409 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd13.04+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~nd13.04+1_amd64.deb Size: 126200 SHA256: 559797dca4e524e49c9fd4dcca57b7aa5302aeec863a28692e6687ddff7d460a SHA1: 9d0948864fb89a075fa8bc1c8fa33dc23259172a MD5sum: f98466ff419625bca964ac0c287be390 Description: OpenGL Extension Wrangler - utilities For more information about GLEW please refer to the description of the libglew-dev package. . This package contains the utilities which can be used to query the supported OpenGL extensions. Package: gmsl Version: 1.1.3-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.3-2~nd13.04+1_all.deb Size: 16296 SHA256: 1330ca8671c52ce66d25a9c4e7342060578d234e9ff8d6d0d2c8d7ee5d4a069e SHA1: 8d7d2c207db0415ea391b037cc2854e453320dbd MD5sum: 1cca8ecb1df3278f8b133028c491886e Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: impressive Version: 0.10.3+svn61-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2), perl Recommends: pdftk Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.3+svn61-1~nd13.04+1_all.deb Size: 155300 SHA256: 159c171633fe48243aa2aa1b42c22cc6bc58f5cdcef3a7c52064018c6795da18 SHA1: 1cd3a49e36b88578c5ac668a511248b23c9f6c45 MD5sum: bb6cdb07320f44bd39856b18f751cbae Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_all.deb Size: 9762 SHA256: 1b70856f5e29f4ff05b8faf310211265c11d0e9b6b78f8bd46101218ed941444 SHA1: 727d17fe0fae14cde238acfc92e4c478e60c84af MD5sum: 60d09f476bd15e66eb89d054f2be4951 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+nd13.04+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+nd13.04+1_all.deb Size: 2408122 SHA256: 65e6c7472a8a92b3ce0c45d17ffb3483e73a2dac7e24c4ba5175cbf31ca7ea99 SHA1: f8cb4f79f753bfd22d7a9a07470b3c8814a656dd MD5sum: 074628f846a92252222964aefd06d892 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4665 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 1286080 SHA256: b735f17a5d75039971d01bafcae7e4eb301a795277414ecebd0064d375dad79c SHA1: a5085547449b24edb309ce17e432dde037a2d232 MD5sum: 0789a3e20beda824754862ced7bcdf5e Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16686 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 7246220 SHA256: f98bd7d37f0204febffa5709f09eb5124cc1f376e53aa7d79d9538ad05cec0f7 SHA1: aa53cd815c0dc66eb3991d7ce8c5fd84b9b4fff9 MD5sum: 811804ffc2f242722732f1bfd66a90bf Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 910 SHA256: e712c2d41ec3b89d4633e58ccdc1f15893b9ac1296d4b9aae04e193ba2aae489 SHA1: 2afc184179ee99033f71f6b7f4cc6ac7e8deff23 MD5sum: 8a3db4e3c7b89648db43bb4aa8dc9b3d Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 834 SHA256: 8dae62722abf91c76d20ca6d926382fa4197efa7d815c1973ab67ade497cd432 SHA1: b39f45f0df6f3fff98b23c54fb2cbadc6db01b1f MD5sum: aca06ab4c7848644ab4e9807593aa026 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd12.10+1+nd13.04+1_all.deb Size: 920 SHA256: 9438c824e04211686d5b9bb4c9a3a5eeedaa619255054650c85642c7e7a97395 SHA1: a1e7af1ada5039a1022157445773c5cf9c931597 MD5sum: af5c4bc8bc33c1c950d56f879b3d1e90 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1712 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd12.10+1+nd13.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_1.4.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 426496 SHA256: 9f4778c20441165a464c88bbe8e8163311791b2c9fd95a44af9c5c4aa1646b37 SHA1: 2072f1cdfcda00abe9380a5416525f368ededfb4 MD5sum: 5a88706d35ae32b0b6a3c1dc9ba6ddf8 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 909 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig1_1.4.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 336076 SHA256: c6f580cf42184ee10bb9cabe5172eecb781ce2e96490e25f20db8b98378d9723 SHA1: a44ce17142e9d0ed071157c83c9b17fa77c3549d MD5sum: 4b62d4b9fcc32a74370f3c9df3718393 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 377 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-1~nd12.10+1+nd13.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig1-dbg_1.4.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 118464 SHA256: c3536259f92d439fe4076f1a67ff00f91ab44b8e2887e6ce76fbcfc402ca89bc SHA1: 823284ab231c23ba0408027323e7b31778283775 MD5sum: b179b01b97c21e41e0c586d6681ca240 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2802 Depends: neurodebian-popularity-contest, libclassad3 (= 7.8.7~dfsg.1-1~nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 521770 SHA256: 8b58dbfe6994c94cdc3dc3998dd38389cf24e41489bbadfbdf85825ab47b9d87 SHA1: c386fbd4cf651b8cb207986d503a2b1f7493a064 MD5sum: cc9307e48dab6cf0b4bbfd8787e899e6 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 883 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+nd13.04+1_amd64.deb Size: 277090 SHA256: e61e0dc580f04e9d67bc29c66d9e490b87d66cdd98ce52c912b3c7171e6ea4dc SHA1: 67032633a9dede6aad15de5e594bad2ed462c714 MD5sum: 571bb758f3d00eb83e388b831915f58e 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~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 632 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1 Suggests: glew-utils Conflicts: libglew1 Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libs Filename: pool/main/g/glew/libglew1.9_1.9.0-3~bnd1~nd13.04+1_amd64.deb Size: 174138 SHA256: 4dd914f7cd82d7a2d73d67ec9a1e3fe5992cdff265f1b543be2c651fa06206c7 SHA1: 53e4d5e9ef0b959c8e73248343be71fd36a56e5a MD5sum: 3691e4a81f9aabbf3c6d80c0dedde97d 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~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 149 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd13.04+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglew1.9-dbg_1.9.0-3~bnd1~nd13.04+1_amd64.deb Size: 41072 SHA256: 9d7c8639b5f79905936b9ea863a0bc6052a54d3613c9e0a6728d0cb972732ed6 SHA1: 90f88a164624e7e2eb27dfa05ad361c0a67ad765 MD5sum: 1c86c69ca3e4b24839985231998e524a 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~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1000 Depends: neurodebian-popularity-contest, libgl1-mesa-dev | libgl-dev, libglew1.9 (= 1.9.0-3~bnd1~nd13.04+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~nd13.04+1_amd64.deb Size: 153250 SHA256: ab3cbc43cc28f44e80d3248dda4ba61fbdb573c0684378393cff48fed81b853d SHA1: 818909da7beb49cf3c84a76d5bf0a2fc01d9964b MD5sum: 3a9ab8d6d9cb6253f32dd2bee3a28cf3 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~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 559 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1 Conflicts: libglew1 Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libs Filename: pool/main/g/glew/libglewmx1.9_1.9.0-3~bnd1~nd13.04+1_amd64.deb Size: 158330 SHA256: 62102d0ccf66f8a10770e119dee9ef7006fe3a5894e51ac0657df56d5df3f467 SHA1: 5ce1e2e8046c4a911814bb3605806fa6c1773b41 MD5sum: d6798ada7dd9bad2dd5077d85e55a37f 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~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 122 Depends: neurodebian-popularity-contest, libglewmx1.9 (= 1.9.0-3~bnd1~nd13.04+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglewmx1.9-dbg_1.9.0-3~bnd1~nd13.04+1_amd64.deb Size: 33058 SHA256: acb3acf8024b2ee08dd92658dedf9c2ece33bc0748f0b008edcd0e8b2348fe7a SHA1: 5a72b2978e08bff3fe5825bbbc622cd659dca38c MD5sum: 6334d8d41cc32bc0a451ec3591094e90 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~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libglew1.9-dev, libglewmx1.9 (= 1.9.0-3~bnd1~nd13.04+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~nd13.04+1_amd64.deb Size: 8836 SHA256: 684aa0a8630b98c4ea1b942050effea611f547b02da5e9957aca8dcaf122e99f SHA1: ef8cbeadd4aed6e8ae8e00530aa901b7cedf7084 MD5sum: 15620bd7a770b3fb9ec32a62cead270d 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25779 Depends: neurodebian-popularity-contest, libinsighttoolkit4.2 (= 4.2.1-2~nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 5275800 SHA256: 977de98947fa6c1fba96d111d3cb313d85a5678d7d18d699998a994b4d592509 SHA1: 20022f0fa9af6968846b7c40c14ca3d71a30474f MD5sum: e39a117b475bb8d7418654938c1c22d7 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21932 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+nd13.04+1_amd64.deb Size: 7144496 SHA256: e33757e5b81b89cc45f1dc76a2d2c088a393a7a341953429a284df4c76091c01 SHA1: 01edb99bc39475d96fde70ee58beb8b67f24e491 MD5sum: 478c09a4c994d757dd4987a150496970 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 2408 SHA256: 79f1a5d0c413d3f1c3b4b357b71c1be47c3be34d2ad5ce6dedc1403d92535ecd SHA1: 654224b931a71b85b68562c2ce378493351d2114 MD5sum: 177b197bbad7894e942e7606c02915d6 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 140 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.31.8), libgtk2.0-0 (>= 2.14.0), libpango1.0-0 (>= 1.14.0), librtfilter1 (>= 1.0) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libs Filename: pool/main/m/mcpanel/libmcpanel0_0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_amd64.deb Size: 49192 SHA256: f4d14fdc152c34b2d1437b37e9eeb02eba55725ccaf30f43e225c292b8446d67 SHA1: 31423202e7ab631a127cf0e79ed0a781788cd3c5 MD5sum: 7e7d95d164a6c121e61f4ff87a19fad9 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 6668 SHA256: c3517b480dd4de7c036cfb18921715de0c668634b4da0c97ef264203e53f3fae SHA1: 3433d65a6a0fce31f95a12e06500f83b7ca4a5b8 MD5sum: 0984961acf147c7b9cb743fc5ccb6205 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: libvrpn-dev Source: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 659 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 174192 SHA256: dcfc795b9c1e2b8647cd18323bef4c5bafa63cd44494aedd9e60aef5a0fbade2 SHA1: 6862adbfd22ebd325d3432a8506e0e5f2eef5047 MD5sum: 0ef37139ffd1047511ae9a186671d9b2 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 535 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpn0_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1_amd64.deb Size: 181978 SHA256: 57084fd03820638db520d3abd4ff390723adafc4830c0485bbfe5557796c34f8 SHA1: 7d0e1cac5d361a081447c273bfdd0f139b36e393 MD5sum: b08b49abc91ca1afa7a62b2af3b270d0 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1355 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpnserver0_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1_amd64.deb Size: 436906 SHA256: 45bb7b34609759d9844432a459bf8906ef754025d0d360f6801ca5de70b41c94 SHA1: 678e2ddabde31ebad82e5dbe4e4ff56f841833ac MD5sum: c2f5da1eed0e4d387c2fb2f13c761ab3 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2529 Depends: neurodebian-popularity-contest, libvw0 (= 7.2-1~nd12.10+1+nd13.04+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_7.2-1~nd12.10+1+nd13.04+1_amd64.deb Size: 574124 SHA256: d4a84f80529bf2ed4ab168268c083d0461e241d4e576dfbbf3f40ba55050c6c8 SHA1: 1565260374b60b83c1f355f2e244787f4f8a49a9 MD5sum: 06dc4568c975f8b876f365f5e02bff15 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 710 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+nd13.04+1_amd64.deb Size: 302866 SHA256: dbb998e36cee70d080e2d87f01f466b2dfc455063cf0159e0f99b7a82f8c4ac9 SHA1: 3699383e76f66b8971fd9d6c79b1b904a627cc28 MD5sum: bbe47db7b7cd200b7831d0c884b02910 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: mriconvert Version: 2.0.250-1+nd1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2781 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.12.1), libwxgtk2.8-0 (>= 2.8.12.1) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.250-1+nd1~nd12.10+1+nd13.04+1_amd64.deb Size: 896548 SHA256: 37f6e5cf03036993f0502f9c1d55d07b101d971ec1c457324dfa41e99f241880 SHA1: c60af7d1f7432581c1da786c9359ab4f9184b6e2 MD5sum: 98ed25869381e447e748a1360018d177 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21362 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libpango1.0-0 (>= 1.14.0), libx11-6, mricron-data Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1+nd13.04+1_amd64.deb Size: 6943310 SHA256: 5edcb2bd5dd11dcff23f4b06fe4fc1576e10c48ddc7e5690eba9bcd17b1733c3 SHA1: 76275326508d34b87138921a4b0369999967493d MD5sum: 38c898b41e0b6ea8032e568e13a12040 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+nd13.04+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+nd13.04+1_all.deb Size: 1664070 SHA256: 8b2a654aad3dc98e12c6b0439b9c4341eaea214734690678a538fb503caa65b2 SHA1: ccfa17f62c2c6680f76e4d9aae1acc557f67a2db MD5sum: 2eb4ee496a8438e325c401235b731544 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+nd13.04+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+nd13.04+1_all.deb Size: 735798 SHA256: 556dd4e3e50a13b8dfceff94cc5496708a250670042cf658dcbbd8de964e0c4b SHA1: a660fee601eb2aabd70f6bd5065c0051ce3f4858 MD5sum: dc44d660cc9199415f1bbfd44b0e801a 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: 0.31~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 142 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.31~nd12.10+1+nd13.04+1_all.deb Size: 115378 SHA256: ea27d4b5413313f3bacf2636461311058dbf67dc2efb94a185ef21a9827b1d9b SHA1: c58ffc48769ca9bb0aa28e29fdeaf99458efc737 MD5sum: 6b4418b053f34c2d176f864e236d7599 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.31~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5762 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.31~nd12.10+1+nd13.04+1_all.deb Size: 5351248 SHA256: 20b3496a3dddd71651fcc1663feba113c1682d212e17dd4cd1dfc1219494fe09 SHA1: 5385e81663517ad533cc9dcef295f0be2a24670e MD5sum: 35409b00fce63b6164ba9e753b5e693b Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.31~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.31~nd12.10+1+nd13.04+1_all.deb Size: 15242 SHA256: 203b9f9d26a55f432e5090e151ce7f09bed96f1329b45d9f9b1d13657859a8e3 SHA1: cfad4d5817354a6ce090b5c6c788b13893fc8e4a MD5sum: 0ca1609f39a61e9c517396686efcbd1c Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.31~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.31~nd12.10+1+nd13.04+1_all.deb Size: 7542 SHA256: afb7778e22dba6945648fe213e8f4cc9c208ae324810845a97c02b32faef7622 SHA1: 6596b8c49b4724805d269908dfc8eecddadbe012 MD5sum: 63b49c8e8f825316a79513eb14a29dda Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.31~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.31~nd12.10+1+nd13.04+1_all.deb Size: 6760 SHA256: 89585a170b94a85bc1241d995296770d8d9f81f6c366da97c308452d540980ce SHA1: d27b7a57312975cc31b0e8cac48f41215b9c2186 MD5sum: 045f0a04c84713469d0107bf73a37ee6 Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom Version: 0.4.6-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2247 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.2, libinsighttoolkit4.3, libstdc++6 (>= 4.6), nifti2dicom-data (= 0.4.6-1~nd13.04+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom_0.4.6-1~nd13.04+1_amd64.deb Size: 496100 SHA256: 8aad8afb8e43e7a0aeaa15f88228894bf780214af5107e7461e9b34281d2c935 SHA1: c5a10a373c597cb86b1f66202ba64aef7f57de0a MD5sum: 50bc7b63f08cb37e6598a1d78393372d Description: convert 3D medical images to DICOM 2D series Nifti2Dicom is a convertion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS. . This package includes the command line tools. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.6-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.6-1~nd13.04+1_all.deb Size: 615142 SHA256: 25fcd552f457b184d04d668f7eac6fce8cc2110021eb8e558717d80cd65cb838 SHA1: c7f09743b5a26140e217e051c47b23230bc1f5a9 MD5sum: b8310ea5e8683fe8af3725b922bca0df Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nuitka Version: 0.4.4.1+ds-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1764 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.7.1-0ubuntu2) 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_0.4.4.1+ds-1~nd13.04+1_all.deb Size: 434390 SHA256: b555701056d13f0fd00a2e03609598d44ed7b2d1e2ff26c8603d050443876c05 SHA1: 05e2a92cc38bc56ab8fefa6f13e7c85b9970b7fe MD5sum: a26ffe2b101bc3487ed52cf24fe4552e Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: octave-biosig Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libbiosig1, libc6 (>= 2.14), liboctave1 (>= 3.6.2) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_1.4.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 24108 SHA256: 7a0ed19b6340589e4949b1a46b9ca29f900bc65e484643bbead515f03919ad7a SHA1: ed4d50cbfab6a21313cb013ca991ab4651cf3912 MD5sum: 9eee30662063f2bd4bec8c59458b8f15 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: 3.0.10.20130612.dfsg1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2725 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.14), libdc1394-22, libfreenect0.1 (>= 1:0.1.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglew1.9 (>= 1.9.0), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.24), liboctave1 (>= 3.6.2), 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 (= 3.0.10.20130612.dfsg1-1~nd13.04+1), psychtoolbox-3-lib (= 3.0.10.20130612.dfsg1-1~nd13.04+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_3.0.10.20130612.dfsg1-1~nd13.04+1_amd64.deb Size: 880762 SHA256: 9dc1185e8a3d8c78b44deac870dba7ff9c8f25d5d0c185e3032a4e0b0110dd53 SHA1: 7322a9c569a4d9ac8e25a5428718577b5e8eff5a MD5sum: f1aceb4c694bc3a2d77d1942b20a3450 Description: toolbox for vision research -- Octave bindings Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . See also http://www.psychtoolbox.org/UsingPsychtoolboxOnUbuntu for additional information about systems tune-up and initial configuration. . This package contains bindings for Octave. Package: opensesame Version: 0.27.2-4~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25510 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.2-4~nd13.04+1_all.deb Size: 24408786 SHA256: 70d0c60552ac2f3317e19cce887599b80549f631c4a32d68c70a8792e76ba918 SHA1: 36e30e60cfcfe4822060bbd0dc4e126b45eb88f1 MD5sum: 281649994df45e17c237b437ef0c6001 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: packaging-tutorial Version: 0.8~nd0+nd13.04+1 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0+nd13.04+1_all.deb Size: 1488406 SHA256: 2080837f62cdfe9c3cdd6abbe134a1672da2a73335dd213fc8c80732244c40a9 SHA1: 15eaae4fe59ba4f53545db6406d8f51e61692eea MD5sum: 27e78cb1c4edc8953cf73e9a71350c36 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.77.02.dfsg-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9277 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-pyo, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.77.02.dfsg-1~nd13.04+1_all.deb Size: 5822162 SHA256: 24503fb0af66619bbcd7c512040ed05d41941e0ed8b8a14d85dd0ea2dc459066 SHA1: 4ba7fde803c919e7d6690214ceef16fe1fdbeb36 MD5sum: 23ecdae1c0f810f0f21fda3ab65bbc27 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: 3.0.10.20130612.dfsg1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49323 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.10.20130612.dfsg1-1~nd13.04+1_all.deb Size: 19881396 SHA256: f73af80559cf179175ff342250500deec28db9d7b7d2793afab3123778f695ce SHA1: a50fcdb1f9f078043aa4de4b1335e27297891ba6 MD5sum: 22fd626844376c8153f774b806ce1909 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: 3.0.10.20130612.dfsg1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2691 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.10.20130612.dfsg1-1~nd13.04+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.10.20130612.dfsg1-1~nd13.04+1_amd64.deb Size: 854918 SHA256: 149b8f24b1f899e3290becc084a5c460c0b35f3b45e1dd8a171d32b1cd5daed2 SHA1: b700ee367233ad8e10123f10603b1b0769b66e30 MD5sum: ea3052cff64a3ecc396fef6b2a39da58 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: 3.0.10.20130612.dfsg1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 150 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfontconfig1 (>= 2.9.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.6) Recommends: gstreamer0.10-plugins-base, gstreamer0.10-plugins-good 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_3.0.10.20130612.dfsg1-1~nd13.04+1_amd64.deb Size: 61408 SHA256: 5cf6fe86257e8242619140ab495daf4f79f967cf12695a130c2bda18ab4379e0 SHA1: 883c7b3cbf26bf6240a9f339217929811526e259 MD5sum: ddebbb42fac277fce4d2cda9b7e90d5b 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 206 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python-support (>= 0.90.0), libbiosig1, libc6 (>= 2.14), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_1.4.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 55788 SHA256: d10b6a9bc04d70f91e0082ad94424b67b4da0e4f2634a58b52d3c794a75cd12f SHA1: c2ffb1d151744381f09e87221926752786176fce MD5sum: 95bee2d6a93715defd6e0c0687a81dd2 Description: Python bindings for BioSig library This package provides Python bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: python-brian Source: brian Version: 1.4.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd13.04+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.1-1~nd13.04+1_all.deb Size: 549182 SHA256: 9eced6619fb84aea48b5a3fcbc2866e03897ae230c42de1363a01e9dd5b54f91 SHA1: 3ca4a641e5ded2df230142d5a767dacb02e2c1fd MD5sum: 1e3f0dd840e5c40b39c063a9ec88305d Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.4.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6811 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.1-1~nd13.04+1_all.deb Size: 2247196 SHA256: d155efe0ac801294667b2a44eb58bcb3b0298e73ff97556f8829de8710828f81 SHA1: c247d3f619f28dacd1fe308e61c674ee3556c2f1 MD5sum: bc1ad205f7e25c67460319c0ce2c290c Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-brian-lib Source: brian Version: 1.4.1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 141 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python-support (>= 0.90.0), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.4.1-1~nd13.04+1_amd64.deb Size: 54142 SHA256: a629ab49125786930a69fcece027e2b223f372b578ff9429c15ab1a4ab7d3d32 SHA1: 165590b96a5e79b3a68bb1ab2a4e21a550f6011a MD5sum: 42fe15ce978cd33a39480f3cfd603d4b 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-dipy Source: dipy Version: 0.6.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2285 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd12.10+1+nd13.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.6.0-1~nd12.10+1+nd13.04+1_all.deb Size: 1586330 SHA256: 9ae0066ea9dd0827c19d4a38ef67434588eb327998c03f91b886f3d0c1ce6e18 SHA1: 6f4fe8d55b808fb237ff4d57616231d396cf1690 MD5sum: 79a32a472ed7bdf1918b72bb9b5a113d Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.7 Package: python-dipy-doc Source: dipy Version: 0.6.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5080 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.6.0-1~nd12.10+1+nd13.04+1_all.deb Size: 3615252 SHA256: 3970b0bb31d6fd6b2c731e67565849f92be42444129498f103111ff56212e1da SHA1: 3dd6d5bb930e50c23924f935a27d73b34ce2f7e9 MD5sum: 0e5cd4baee579fba43b730871827fa4f Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-dipy-lib Source: dipy Version: 0.6.0-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 942 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), libc6 (>= 2.14) Provides: python2.7-dipy-lib Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.6.0-1~nd12.10+1+nd13.04+1_amd64.deb Size: 373070 SHA256: a85af344232a3f80271c125ea25b7388de9297b3f58f97eea004f05a814c1e07 SHA1: 9351288484bd512461fceb03cce3a503209b48cd MD5sum: 71f2a6af85becbea40051c71cbc1c2f6 Description: toolbox for analysis of MR diffusion imaging data -- extensions Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Python-Version: 2.7 Package: python-joblib Source: joblib Version: 0.7.0+git14-g3e5784c-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 182 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.7.0+git14-g3e5784c-1~nd13.04+1_all.deb Size: 54662 SHA256: e3297f7f52b76c95ce9848260ac4bd6291156ebe70d79e88146ca6e7d6dbad59 SHA1: 2acacc1ee95aea02a23a7600ac41da7ac90f58b0 MD5sum: 2af0d5bec6b033809d6ae6bd0ebd59ce 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+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1495 Depends: neurodebian-popularity-contest, 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+nd13.04+1_all.deb Size: 478658 SHA256: f64b49dd6826c89a25593bc9a05d154599fed318b5bb9a3c8e99c6ce9ba6dd5a SHA1: 1d63f0f452c4d60af96494263e7f75b724ee7a56 MD5sum: 4d9fb528cbf2c338a7562b20dcfd8a4d 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-mvpa2 Source: pymvpa2 Version: 2.2.0-3~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4242 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-3~nd13.04+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_2.2.0-3~nd13.04+1_all.deb Size: 2400384 SHA256: 474324cdfe989a5ddd084222baf761d0b27a5202532fc64d68cf92c3c06bc8b0 SHA1: 63155a01d54d39adb0f97b11d3fee03172489002 MD5sum: 2f8b981072cb0e2120acd3e12f4c3455 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: 2.2.0-3~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17216 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.2.0-3~nd13.04+1_all.deb Size: 5138256 SHA256: 0dac0291a99608f696551dedb0643df38d033792ea0ef02ca3bcf02e4bd265cd SHA1: a93621906b6b7f7634c6515d4c68e6a706ba325e MD5sum: a3e0d1b142a97bb3d997496fafccfac4 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-mvpa2-lib Source: pymvpa2 Version: 2.2.0-3~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 112 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), 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_2.2.0-3~nd13.04+1_amd64.deb Size: 49734 SHA256: 12463d86a9d04690d248e85d15cc1ea386997c41e4dc19dd6ade8b1038446ed8 SHA1: b09a86c10636fbfec275b2f1565a321a19bd5b82 MD5sum: f6f8c5c380ff2b8ab8cd37d92b76415f Description: low-level implementations and bindings for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. . This is a package of a development snapshot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.7 Package: python-neo Source: neo Version: 0.3.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2452 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.0-1~nd13.04+1_all.deb Size: 1434290 SHA256: d617c8562aef8a93d87b81f786515713e183b69f3b12ccacfa81a31cc1ca6072 SHA1: 881201b90e5756e306f2b703398685cf001ab1c7 MD5sum: 91c54bceee7f1ffa5af4abf3c8adec1a Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-neurosynth Source: neurosynth Version: 0.3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd13.04+1_all.deb Size: 32522 SHA256: 6f15ee7476bbfde85706a4c2f3d03cda6040abdbd999b3894242c38a6b5e5e15 SHA1: 6d50b9fdc6cc95d65b5a36dff938dc20a53f85a6 MD5sum: 490b03520a88f2609b103c8f9e81d538 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_all.deb Size: 1816392 SHA256: 5471327f1b831976c7f5fd2e640cea5ad293f6fe3d5dc5c90cad27576dd31c7b SHA1: 24a7b6779e812cb2e0141392940f64f5f9654eab MD5sum: c0d6269ba81e7cde8a02b4ecf5d09a9a 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+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2446 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+nd13.04+1_all.deb Size: 441852 SHA256: c393586f5d860ad1367c994624f64e56f5142eb810b1c7eb398b3a4514ba1e59 SHA1: d709ae60593669a10a6ba1bce7e3597b4ec0cdf9 MD5sum: debae32aab1b2c3078bcd33a7e0a3dbd 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-nipype Source: nipype Version: 0.8-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2657 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.8-1~nd13.04+1_all.deb Size: 591950 SHA256: 2f812b136b34da77235d0f3536820e7056c1335309e1f87e47a6a125c1d90adf SHA1: 5b406227d3e557d7df9b1067f72214a8a8ea8118 MD5sum: 4ea3dd23e9379c15860dc677ff035ae1 Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.8-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15049 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.8-1~nd13.04+1_all.deb Size: 7167192 SHA256: 88f01a903b608aee3333ea08dde51349fa3a4f52444bc9f000244d6054d747d5 SHA1: 28d42e2f21fce48fb3238a4eb8376d31d4592227 MD5sum: 291cfb0012df032c615e4798889b37fb 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+nd13.04+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+nd13.04+1_all.deb Size: 62100 SHA256: 64a8cf4cf5747a7cf77d347a415106897b547e7a88668496a480061112af091d SHA1: 7c45f18602568f0d607cc9f0ed352f9008afdc55 MD5sum: 70aa1ace74bb64e286215aeb426e1f0d 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: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4619 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.11.0-1~nd12.10+1+nd13.04+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_0.11.0-1~nd12.10+1+nd13.04+1_all.deb Size: 947182 SHA256: 1d5e56a393bd34ff67b28b6f59ee31db3cebc53fc75835b478d5763c5fdc6356 SHA1: 5df4d1e198b2a0954b67e0730a80dcddff9238da MD5sum: cc689361372a1bb68c42155bddbfee54 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: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4093 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Provides: python2.7-pandas-lib Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas-lib_0.11.0-1~nd12.10+1+nd13.04+1_amd64.deb Size: 1528078 SHA256: ba8eca1d59e3245238cb4fa080d353c05f06a8a4dfebfb55fb677fe32ded259d SHA1: cf196564b65b92fdd7015afae496a8150edd64a5 MD5sum: ef37175daed439d6649ae2ba32698c09 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-pyepl Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1386 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-pyepl-common (= 1.1.0+git12-g365f8e3-1~nd13.04+1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0 (>= 0.1.7), libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.7-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0+git12-g365f8e3-1~nd13.04+1_amd64.deb Size: 381666 SHA256: 7c2a2366972e0cecd02848670ba663ea13a650607693024366771044caf2c989 SHA1: d06fa303328d503d256b21e9d33b9e64844fa194 MD5sum: fc2e8b16cdd6574d99d042b7cb4ef22b Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides PyEPL for supported versions of Python. Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-1~nd13.04+1_all.deb Size: 818176 SHA256: 097efc4d06130e31918954c3e901e400f237e0f475598c979ba793b22f00fee9 SHA1: 397ed10a3443352ff557247581b24fe80e384df1 MD5sum: 3a0d6e4d45b2976e08f0902d1656d9f2 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pynn Source: pynn Version: 0.7.5-1~nd12.10+1+nd13.04+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+nd13.04+1_all.deb Size: 175824 SHA256: 8739a9484b7006b204e2cd7d530501ee969178ae5f0e291ef1d7243a6a5cfa6b SHA1: 6a89717511066991e428fc8e7efec413b51d4008 MD5sum: c6787a9174e58de883cd75f9682891f7 Description: simulator-independent specification of neuronal network models PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-pyo Version: 0.6.6-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10239 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), libc6 (>= 2.14), liblo7 (>= 0.26~repack), libportaudio2 (>= 19+svn20101113), libportmidi0, libsndfile1 (>= 1.0.20), python (<< 2.8) Recommends: python-tk, python-imaging-tk, python-wxgtk2.8 Homepage: http://code.google.com/p/pyo/ Priority: optional Section: python Filename: pool/main/p/python-pyo/python-pyo_0.6.6-1~nd13.04+1_amd64.deb Size: 4992968 SHA256: 1b6293b7a6805899fbdab28617fa39e82708017efcd53fe72f125d9e59f77ef1 SHA1: 22ff04c99049c743a29262115436b4a08d9fdf01 MD5sum: 1a0a0dbd665ba3047b64e074416b918b Description: Python module written in C to help digital signal processing script creation pyo is a Python module containing classes for a wide variety of audio signal processing types. With pyo, user will be able to include signal processing chains directly in Python scripts or projects, and to manipulate them in real time through the interpreter. Tools in pyo module offer primitives, like mathematical operations on audio signal, basic signal processing (filters, delays, synthesis generators, etc.), but also complex algorithms to create sound granulation and others creative audio manipulations. . pyo supports OSC protocol (Open Sound Control), to ease communications between softwares, and MIDI protocol, for generating sound events and controlling process parameters. . pyo allows creation of sophisticated signal processing chains with all the benefits of a mature, and wildly used, general programming language. Package: python-pypsignifit Source: psignifit3 Version: 3.0~beta.20120611.1-1~nd12.04+1+nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1595 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6), python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy Homepage: http://psignifit.sourceforge.net Priority: extra Section: python Filename: pool/main/p/psignifit3/python-pypsignifit_3.0~beta.20120611.1-1~nd12.04+1+nd12.10+1+nd13.04+1_amd64.deb Size: 445714 SHA256: d3d0f100c8fff1efa854a63dbbe6750b80bd2cc81832fbd71834866d608d27fe SHA1: 238bae4094d300fe346e7ada48368575be6e7b4c MD5sum: 8c8da4fd139fb24a87ab78628092f40f 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+nd13.04+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+nd13.04+1_all.deb Size: 190396 SHA256: a742f5f47842ee38e7491416ea0de9e721716c69c4cf32cae51ad2367a8d89c2 SHA1: c687df5e68cc79fba85924061a7f8e3d9732903e MD5sum: 4e5a040be6b4430eab9ed7922df103ee 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: 0.13.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.13.1-1~nd13.04+1_all.deb Size: 28612 SHA256: 49b381688b868373dc5aac2e6659e9a486e8a547ce4402514b96edcf8ee2688a SHA1: 1199807fd23ac89eddf6bf4c6549682169f14030 MD5sum: b9891a3d444187608834e53d6b12b9c8 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: 0.8.2-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4550 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.7, python-scipy (>= 0.10), python-skimage-lib (>= 0.8.2-1~nd13.04+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging, python-qt4 Suggests: python-skimage-doc, python-opencv Provides: python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.8.2-1~nd13.04+1_all.deb Size: 3236962 SHA256: 9bba8581a76821b750842065aeaadefd18cf87444f083026aad7b71d4c208fea SHA1: 4561189865362a95d0747d0ed70c480f4f30b9a2 MD5sum: 37e1f116232577ff2ddcbb39457d7759 Description: Python modules for image processing scikits-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. Package: python-skimage-doc Source: skimage Version: 0.8.2-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14178 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-skimage Homepage: http://scikits-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.8.2-1~nd13.04+1_all.deb Size: 11797626 SHA256: 62e1fb720acfcbfe453c0b474f00295bff75e43d2e858edd4b1bf4daf4929509 SHA1: 6e76af47937ce84a4ceb778ae9328d180bc2a9f0 MD5sum: 11ba074b0d482f12c6aa877aa0908323 Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-skimage-lib Source: skimage Version: 0.8.2-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2618 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python-support (>= 0.90.0) Recommends: python-skimage Provides: python2.7-skimage-lib Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage-lib_0.8.2-1~nd13.04+1_amd64.deb Size: 969376 SHA256: fa93308b4dad9e5048f10ba0982ddab3bbc0cdbe57dd3f5cdd8d695240f0c475 SHA1: dbc65769e773891b1558c337e6aad0f28ff165c0 MD5sum: eae170ea8d6be5d9dfe8560f1bff7c61 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: 0.13.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3050 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.13.1-1~nd13.04+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.13.1-1~nd13.04+1_all.deb Size: 1012812 SHA256: d31a9938481ca9caeb8cb44e4b86f0f8c3b9625aee7ee21f1b451fb4cc39da43 SHA1: d09268ee24403ad009ece00d38640350db3d0744 MD5sum: 8456874a950c99b6227520ff47698474 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Package: python-sklearn-doc Source: scikit-learn Version: 0.13.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42069 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.13.1-1~nd13.04+1_all.deb Size: 30685864 SHA256: f767980a091ddcc6430bd71e66764e6b04fe688895a39313d14852622afe94ce SHA1: d7c4457a67d1b48149e23b5a1af51b33c46e4383 MD5sum: 52489b600f7b5505ca1de75c11afa2f2 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sklearn-lib Source: scikit-learn Version: 0.13.1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2547 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.7-0~b1), 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_0.13.1-1~nd13.04+1_amd64.deb Size: 1006440 SHA256: 63d995ee6d702de78f595aa430016594010964c884666784d1a398ba42fc8c64 SHA1: 410dd047e02f45f60561bb2761d5ca1e08b75d10 MD5sum: e26365c2a67ea67632558e96a8b6cd93 Description: low-level implementations and bindings for scikit-learn This is an add-on package for python-sklearn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Package: python-spykeutils Source: spykeutils Version: 0.3.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1977 Depends: neurodebian-popularity-contest, 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_0.3.0-1~nd13.04+1_all.deb Size: 392242 SHA256: e0d03ca9e1ed36dc123e0085352f11fb8b947a1b5141725d904f454e0a09689b SHA1: b0bbc87a2ae9b34c5b628135ce106048ff4ab1e3 MD5sum: c3cfbe916a7523d03d5416e06104f7ff 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-stfio Source: stimfit Version: 0.12.1-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 815 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7.1-0ubuntu2), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, libbiosig1, libc6 (>= 2.14), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libhdf5-7, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1) Recommends: python-matplotlib, python-scipy Provides: python2.7-stfio Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.12.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 270510 SHA256: 76695eb3b1d6adb35ec72ae22e4ae6bf143f5a64078e05f2e44b4d91dd1fb541 SHA1: 6687ab0b672ee564d04663ae2e4231ab0d99896a MD5sum: f510703bc59dcc7e378d057cb98a2932 Description: Python module to read common electrophysiology file formats. The stfio module allows you to read common electrophysiology file formats from Python. Axon binaries (abf), Axon text (atf), HEKA (dat), CFS (dat/cfs), Axograph (axgd/axgx) are currently supported. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_all.deb Size: 28126 SHA256: d027822bf6edf7f8e4a4726abfa4bff05a553010e617229cf66c9ebc39f260ae SHA1: ae3d0fa877af7818bb783d737f8a8df882765042 MD5sum: db1308bda1624dc4cacc037c4519bbc7 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+nd13.04+1 Architecture: amd64 Bugs: mailto:bugs@neuro.debian.net Maintainer: NeuroDebian Team Installed-Size: 1679 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0) Suggests: python-traitsui Conflicts: python-traits (>= 4.0~) Homepage: http://pypi.python.org/pypi/traits Priority: optional Section: python Filename: pool/main/p/python-traits/python-traits4_4.0.0-1~cbp1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1_amd64.deb Size: 342836 SHA256: 213c6224e36513eec318e67e47e29c24ff908194c0c817a78454ad47ad4b970a SHA1: 2f9f9802925932f03e1678d138a16977dfdc3bfe MD5sum: 3ee1f2da8b90698052ab222648a7b52c 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-visionegg Source: visionegg Version: 1.2.1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1738 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgl1-mesa-glx, python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://www.visionegg.org Priority: optional Section: python Filename: pool/main/v/visionegg/python-visionegg_1.2.1-1~nd13.04+1_amd64.deb Size: 666584 SHA256: dc308915c93ede96477a387cf787dcd5339d3528299c30bc205697cfbae7c06f SHA1: b9a10cc529661bf8ef98876cbab56c75892129ee MD5sum: d4c89cb6a71f52ca7f17b005a3162020 Description: Python library for 2D/3D visual stimulus generation The Vision Egg is a programming library that uses standard, inexpensive computer graphics cards to produce visual stimuli for vision research experiments. Package: python3-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.10+1+nd13.04+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+nd13.04+1_all.deb Size: 472534 SHA256: d3239c36162185a4ef03148ce61949c6f7a9e74670d56c88722e4200721b57dc SHA1: 2562033ca9918601e8f0cd28eff8146a5a610d52 MD5sum: 735efdc181c99b658b0e9276ea66e384 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-pandas Source: pandas Version: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4567 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.11.0-1~nd12.10+1+nd13.04+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_0.11.0-1~nd12.10+1+nd13.04+1_all.deb Size: 943432 SHA256: 9eb4b9222b38f3ff7dc2e51e126e539e2331b594f84e760f02dc922d73b32aef SHA1: 8d7618a58b18651f92ee220839aa77e1e3aed952 MD5sum: a586d026af85a2c6d4a651e3e92467d1 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: 0.11.0-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3973 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python3-numpy (>= 1:1.7-0~b1), python3-numpy-abi9, python3 (>= 3.3), python3 (<< 3.4) Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas-lib_0.11.0-1~nd12.10+1+nd13.04+1_amd64.deb Size: 1493184 SHA256: 6462ee6eb23dfebd23dee47b2ac9cf0bf45d040e1c3035e7e6b06b5636d1182a SHA1: 3cb37fc050d56ae5dfd9ed339dc006ceff223296 MD5sum: ecc40a329e43c85f8b75d3154cb684f7 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: qnifti2dicom Source: nifti2dicom Version: 0.4.6-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3195 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.2, libinsighttoolkit4.3, libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6), libvtk5.8, libvtk5.8-qt4, nifti2dicom (= 0.4.6-1~nd13.04+1), nifti2dicom-data (= 0.4.6-1~nd13.04+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/qnifti2dicom_0.4.6-1~nd13.04+1_amd64.deb Size: 685928 SHA256: a44a96a7cf81fc3068fbe4d4635107b4f0c6af0681e895513510c9f7f120adb4 SHA1: 935cf22769612e24f56bb6b7a718e4482c21ffae MD5sum: 4f211f79c05f6b8696e96b3f7b5bc2cc 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: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18499 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+nd13.04+1_all.deb Size: 10744012 SHA256: 8cbaf2ef8a3621e1a45f8046a5a4b3f5018599cd5fd061dbdab3f6f636db55dc SHA1: 358a47eca3c2a8e45383012ebb1db5ed7e315d4f MD5sum: 2ea4c1626f3ecc1a0f8b72afe93d7325 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+nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72987 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1_all.deb Size: 52166644 SHA256: adf5ccd28c33d360fddc672827cc45f128dd95d107e6fc80ec7b5f01013199b8 SHA1: 555fd27c1066c8cdc3f9d5ca88e1a5604af0f318 MD5sum: 007b37221423d90e041d57b47f112413 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+nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9242 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1_all.deb Size: 8990888 SHA256: 26ace888d49d3abb1c4525fff11e3538aaf54bdeb7bd0f37720b5a32c211055d SHA1: 3e7bd77be9b08a8227586a6138fba786fa7a5365 MD5sum: 823a54d07acd71360de860ff3f62e019 Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: spykeviewer Version: 0.3.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 933 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.3.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.3.0-1~nd13.04+1_all.deb Size: 491216 SHA256: ef5605c2b3d24d24683974f3c50df84d11df18c7a407a4832d2dd08063639c5f SHA1: 308aead772eb311b2fa71e6819aa1d4a52996f1b MD5sum: 403fc66facd04dc4937fd3ebd412784d 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+nd13.04+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+nd13.04+1_all.deb Size: 28806 SHA256: f4d0a92bd82ccf13995bd023e60482c9bb6e5bcc276aacf8d6d71f47c726ac91 SHA1: 5d37dfd09ba13dca96797dc779dfd58616624062 MD5sum: 64c06762e13f6bd6a33f11383612ccc0 Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: stimfit Version: 0.12.1-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2538 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.14), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-double3, libgcc1 (>= 1:4.1.1), libhdf5-7, liblapack3 | liblapack.so.3 | libatlas3-base, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1), libwxbase2.8-0 (>= 2.8.12.1), libwxgtk2.8-0 (>= 2.8.12.1), python (>= 2.7.1-0ubuntu2), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python2.7, python-wxgtk2.8 (>= 2.8.9), python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.12.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 832914 SHA256: 409561093f145ab62b3ad75fc0dd1d0b225bc57bc87046ba4810b779e6d48cb1 SHA1: fa87147c1e0920b55d330ef5155ac70fefbaccb4 MD5sum: f36d354a261ad79263f1c183dc003bca Description: Program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.12.1-1~nd12.10+1+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15197 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.12.1-1~nd12.10+1+nd13.04+1_amd64.deb Size: 4425408 SHA256: 44befbdd6a2bcfdbc17d86e778a5b3ab6cb442d8ce7fac53c65b2a5ff03462c6 SHA1: 2381614464a312c1492081b2ea7ce59ffde183e3 MD5sum: 69c302dd6892e2cce84d11f097025614 Description: Debug symbols for stimfit Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: testkraut Version: 0.0.1-1~nd12.10+1+nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 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+nd13.04+1_all.deb Size: 99674 SHA256: 4bb183a82062eea15a25438731463d7d3a57ac1fd8bbe3f38101b6c3c08c6db2 SHA1: 045ce201ed946332c8971bc0b8f92774aa49e90b MD5sum: 7b33e1503852f85c3d82cd5c0d1c0e54 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 51 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libvw0 (= 7.2-1~nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 21124 SHA256: f4361428185d91fa69e5c6eae7de99a3774540a4e54441e36752aabba44cd34a SHA1: 3818a666e4beed8d9f735375dd8400b33f93765b MD5sum: c43295d14d9da41aa0a843dc6c02cef3 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8012 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 7.2-1~nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 2345704 SHA256: ddc4f5f003e227ce49070aaf076630983f2757a7c96e3acab0facc3f43d8c8a2 SHA1: e88faa6beeacac7f1d4a2c9111d8a7752582fea3 MD5sum: f935649683ac54dc847310143047efcc 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+nd13.04+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+nd13.04+1_all.deb Size: 50202306 SHA256: bff603a80e099779a6e59e179b519f491ae3a21a34e554d4271d9989c5786d1c SHA1: d574744d3ecb4bf57b35726a13848d5b09cc33bc MD5sum: 1d0fb1795be60a140cf7eba2b98ccbe5 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 323 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+nd13.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 66056 SHA256: 7e9244bf8026751b964571062d5cef7e3623b86853a9fe91505491790a45d333 SHA1: e5a92f877e8d69f82bb6cad0e44d1fda29bffd90 MD5sum: e29bb265adb354b9d42239bfe8ae614d 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+nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5732 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1), vrpn (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+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+nd13.04+1_amd64.deb Size: 1767192 SHA256: 290a4a45f41649edbed7dd832f79577eed77e9c62591bee85915acc566c3f118 SHA1: 59ab62fc07e4776fef8681ff8ad79b4511ce2587 MD5sum: 56e377e6beec84b247002b4cff5b9766 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.