Package: aghermann Version: 0.9.1.1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1401 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_0.9.1.1-1~nd13.04+1_amd64.deb Size: 626836 SHA256: c5a8a65a09ce0b04674d564dde729ed243bcfd31bc6ff45bc7b913fabf61724c SHA1: bd4d00179a858170c1bdf2d4b5d2f8adb39ebd7e MD5sum: 029bc1274f9c28359f6e2cf4999e6684 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: cnrun Version: 1.1.14-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libreadline6 (>= 6.0), libstdc++6 (>= 4.6), libxml2 (>= 2.6.27) Suggests: gnuplot Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun_1.1.14-1~nd13.04+1_amd64.deb Size: 126540 SHA256: 328aed68527c66c624e641b48cfb0d67b9ca4e6cab75b66b9b3a5ee3ed1ca72a SHA1: 00e7f315a0b912d2c813ae93fb6f14c82be4bbff MD5sum: ce3192ad18086e7a12084e12140d1191 Description: NeuroML-capable neuronal network simulator CNrun is a neuronal network model simulator, similar in purpose to NEURON except that individual neurons are not compartmentalised. It can read NeuroML files (e.g., as generated by neuroConstruct); provides a Hodgkin-Huxley neuron (plus some varieties), a Rall and Alpha-Beta synapses, Poisson, Van der Pol, Colpitts oscillators and regular pulse generator; external inputs and logging state variables. Uses a 6-5 Runge-Kutta integration method. Basic scripting and (if run interactively) context-aware autocompletion. Package: condor Version: 7.8.8~dfsg.1-2~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 14728 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.14), libclassad3, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-common0 (>= 14), libglobus-ftp-control1 (>= 4), libglobus-gass-transfer2 (>= 7), libglobus-gram-client3 (>= 12), libglobus-gsi-credential1 (>= 5), libglobus-gsi-proxy-core0 (>= 6), libglobus-gsi-sysconfig1 (>= 5), libglobus-gss-assist3 (>= 8), libglobus-gssapi-gsi4 (>= 10), libglobus-io3 (>= 9), libglobus-rsl2 (>= 9), libglobus-xio0 (>= 3), libgsoap2, libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.10+dfsg~), libldap-2.4-2 (>= 2.4.7), libpcre3 (>= 8.10), libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.6), libuuid1 (>= 2.16), libvirt0 (>= 0.5.0), python, perl, adduser, libdate-manip-perl Recommends: dmtcp Suggests: coop-computing-tools Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/condor_7.8.8~dfsg.1-2~nd13.04+1_amd64.deb Size: 4915752 SHA256: 6a8220867b68acfc3afe69a925c19b8f4cb5331faaccf234177e03479b5df072 SHA1: 7f11850fe402e9ee4e25ccb147181bff695c51cf MD5sum: 74cb77b0635d6fea48aa97eaff4ae085 Description: distributed workload management system Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor; Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package can set up an appropriate initial configuration at install time for a machine intended either as a member of an existing Condor pool or as a "Personal" (single machine) Condor pool. Package: condor-dbg Source: condor Version: 7.8.8~dfsg.1-2~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 35971 Depends: neurodebian-popularity-contest, condor (= 7.8.8~dfsg.1-2~nd13.04+1) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: debug Filename: pool/main/c/condor/condor-dbg_7.8.8~dfsg.1-2~nd13.04+1_amd64.deb Size: 12415404 SHA256: 40badc4d29909feb5e06c9d0248238c35e2198eb8bfd2f41a8c4ecdd921cbebf SHA1: 6f982647fdf446eb3ec08b2249e65cdf8bcf057d MD5sum: dcba7da5e32f9087da1b5089b19cb1ac Description: distributed workload management system - debugging symbols Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor; Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides the debugging symbols for Condor. Package: condor-dev Source: condor Version: 7.8.8~dfsg.1-2~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 2068 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: devel Filename: pool/main/c/condor/condor-dev_7.8.8~dfsg.1-2~nd13.04+1_amd64.deb Size: 459920 SHA256: ce7d1be66b76c0ed70607f52bac298b70c1e67b9f35fbf2e8ec21236f34cf603 SHA1: 46538d3cea88fbcb3c8cc89bbc2f350eeb82c2fc MD5sum: 18683276bed478ab9b218747f74fc09f Description: distributed workload management system - development files Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor; Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides headers and libraries for development of Condor add-ons. Package: condor-doc Source: condor Version: 7.8.8~dfsg.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6118 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.8~dfsg.1-2~nd13.04+1_all.deb Size: 1459864 SHA256: 70fd12501ed9c750b5e148e55bc2335501daaaf6b77ff80dc728277c4a68f4f6 SHA1: c659a0a5d89550d08188b5f04afba63c467db9fd MD5sum: 32fab8d8211465476741f9d83cf1ec9e 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: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11748 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: ipython1x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython1x/ipython1x_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 4486534 SHA256: 94324cd485722db64cddadc7bfd1ebf13e71ff6e45c532a24dfd4952f380a287 SHA1: f6f1a41353f31ba8f0886ca23e778e989db1aec9 MD5sum: 9362cc9f7293e4a0fb8597dc71456e6f Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10403 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 4191858 SHA256: fbeb5290528cfca96b94b65cbfa132d97608f6f5a651294aed932d7423b27bf3 SHA1: d6f03dbf53b09e44d6b43a614646a182e75fc7c2 MD5sum: 9b59122dee64e866196af3d1690393ae Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 914 SHA256: ab5fbda2005a1d46b4250a354bc3448b549f11c15b8b1676d224361f108f7ba7 SHA1: 7242d729a32fe408f059430a5968889d0ee717f6 MD5sum: 05a7f10ee24405419cf88a47f39ddd71 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x-parallel Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 838 SHA256: 43315410a21249c1c7d2d5a4ebdba410e73469d8af202da572e571c6159adc29 SHA1: 723104e9bf1bde1e5cea3eef9e7e34a0c5a10c7e MD5sum: 3a7f4a7e5c24c95d2e5c3f08a5815d3d Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.1.0+git7-gf5891e9-1~nd13.04+1_all.deb Size: 926 SHA256: a0c02b115f2b0311d514a7c1752b7d93847cc8d1a0947ae9e730cf7245590b06 SHA1: d8425631e03d417deb9841e72d2cddb0f89ea711 MD5sum: a4d2ae8a03caea83f7430a598f7b0d4b Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: libbiosig-dev Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+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: 7.8.8~dfsg.1-2~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 2834 Depends: neurodebian-popularity-contest, libclassad3 (= 7.8.8~dfsg.1-2~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_7.8.8~dfsg.1-2~nd13.04+1_amd64.deb Size: 523618 SHA256: 4a34f322c1f24035e02f5525260bd2d26a549e88fde9f53c6cabaecd4c2bc343 SHA1: 9d826009050f94080ffc4d45ceec23d5fa690452 MD5sum: b89b38e57d0b1132c4ab6ee556565e05 Description: Condor classads expression language - development library Classified Advertisements (classads) are the lingua franca of Condor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which is used by the Condor central manager to determine the compatibility of jobs, and workstations where they may be run. . This package provides the static library and header files. Package: libclassad3 Source: condor Version: 7.8.8~dfsg.1-2~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 912 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libpcre3 (>= 8.10), libstdc++6 (>= 4.6) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/libclassad3_7.8.8~dfsg.1-2~nd13.04+1_amd64.deb Size: 275430 SHA256: 2b6e7a7569125db9fd5cd6f5c6a9c9023309b15df02be48e481c516b7ed7c9bb SHA1: b694eed2a370dcb69724310e8c77258df9a42287 MD5sum: 54e9abe8bbcb8eaa4f6f81dc0c9c482c 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: 7.3-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2576 Depends: neurodebian-popularity-contest, libvw0 (= 7.3-1~nd13.04+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_7.3-1~nd13.04+1_amd64.deb Size: 579704 SHA256: 21830c821c7bd7fcbee2b685da1ed4f2929e4231cec75aeb074d4a1fc3d050d3 SHA1: 74e0046c94562d824457909f49ddcfacd096b1e4 MD5sum: 14ad918d1ba6ce9eb050fbee93284bb0 Description: fast and scalable online machine learning algorithm - development files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains development files needed to compile and link programs which use vowpal-wabbit's libraries. Package: libvw0 Source: vowpal-wabbit Version: 7.3-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 726 Depends: neurodebian-popularity-contest, libboost-program-options1.49.0 (>= 1.49.0-1), libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.2.3.3) Homepage: http://hunch.net/~vw/ Priority: optional Section: libs Filename: pool/main/v/vowpal-wabbit/libvw0_7.3-1~nd13.04+1_amd64.deb Size: 305342 SHA256: 5533fd8aed2b86d650775d5741483bf60f7518ab7cb88359ffd1eca987f720b0 SHA1: e5a3301d907464ca6b0d98168e7a1b74df1611d8 MD5sum: a447b85f557c4b7ab2e5183d60c72abd 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: 0.20130828.1~dfsg.1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 19646 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libpango1.0-0 (>= 1.14.0), libx11-6, mricron-data Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20130828.1~dfsg.1-1~nd13.04+1_amd64.deb Size: 6408202 SHA256: da256301a6571339de8f06ef00c0fb98434d6f34e84c9cb0e76b0b1accd53a4a SHA1: 1408df2bc5c801a6e018b0b025f68033854507b4 MD5sum: c813a4662b4ccfd74e8df3a184d4142b Description: magnetic resonance image conversion, viewing and analysis This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20130828.1~dfsg.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1710 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20130828.1~dfsg.1-1~nd13.04+1_all.deb Size: 1664524 SHA256: 179f46350fc86243f6af340e0ba92f4d8cbafb46d0c085be129f29f8ac846159 SHA1: cfa43237268d891d2472016ee77e8c7470298c30 MD5sum: cb9e0cda7870731a8d15ff9e016e002b Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20130828.1~dfsg.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1019 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20130828.1~dfsg.1-1~nd13.04+1_all.deb Size: 737510 SHA256: f6522a77a07ff991e70a16fbeb0a146e3744049373534f465c99da6da03aabe7 SHA1: fdfd8b08dfb6c00c23ceb6b8b139ba16d80e09a1 MD5sum: c16e9f95bb777af9a6fa7517187931d4 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix Version: 0.2.11-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 8379 Depends: neurodebian-popularity-contest, libatkmm-1.6-1 (>= 2.22.1), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.33.13), libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1c2a (>= 1:2.24.0), libsigc++-2.0-0c2a (>= 2.0.2), libstdc++6 (>= 4.6), zlib1g (>= 1:1.1.4) Suggests: mrtrix-doc, octave, matlab-support Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: science Filename: pool/main/m/mrtrix/mrtrix_0.2.11-1~nd13.04+1_amd64.deb Size: 2622466 SHA256: 8dc632141a76fdee8c961ac77eac1b0320c9ec849536a549ec419aee20a61848 SHA1: a30453bf5bb3c397a2f30e027af7e3a93652a65e MD5sum: cd877365b1c4bd2b1e4615d5fcc96ed6 Description: diffusion-weighted MRI white matter tractography Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. Package: mrtrix-doc Source: mrtrix Version: 0.2.11-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3520 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.11-1~nd13.04+1_all.deb Size: 3317828 SHA256: c2fdca5d4469af324b396660001238f4289922a55cc45813d5bc97060df0fab8 SHA1: 2c21e943a5b80e042f73fdfd2b768fe556fe1110 MD5sum: dfa6593cfe9bb6d76eb51cd2de717334 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.32~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_0.32~nd13.04+1_all.deb Size: 115558 SHA256: fb2ce5fb8bdc68b9538ded1fe8b38d840b2070be56005c60fff83a190d84bd7a SHA1: 8e4efae8e33e6bafcfd3656f9036801ce96c04fd MD5sum: abebd793bb8fd1467bdda10a7a65fe87 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.32~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6842 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.32~nd13.04+1_all.deb Size: 6433088 SHA256: 57c6da06449724a2baf893712cdaa5f8ba7d0ce73c335c643788d70775686b01 SHA1: 3f6a9999cc43c704b76e480da1d7041a90e88a51 MD5sum: f3c750d80bce68b591bd1c44c72c6540 Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.32~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_0.32~nd13.04+1_all.deb Size: 15368 SHA256: a17f7ae3ceef942087450f8d19e2b87b142f5787ec1981beb22a29499f735bde SHA1: c801838145de1ee7c04f30b6f725c5922174205d MD5sum: e931791983e6ca2f27adfa619982b7d0 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.32~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_0.32~nd13.04+1_all.deb Size: 7624 SHA256: 239d23454e0e47195450883b29b63d8f417dd5719c390765a69518f4e6af075f SHA1: 8e8b3228adaa59e3178d615bf671324fb96e8eb6 MD5sum: f7f0dd6a1bc84b7d45235919ecc716c0 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.32~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_0.32~nd13.04+1_all.deb Size: 6842 SHA256: 227ff47eaa0092133dd93682f02f830406c759d1af8ce4f4fc197e77cb62aad7 SHA1: 2cb7bc299aef28713e49f41b48191dfee2f59291 MD5sum: 75e389175704744299d52ede1a19e280 Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom Version: 0.4.6-2~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 (= 0.4.6-2~nd13.04+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom_0.4.6-2~nd13.04+1_amd64.deb Size: 496144 SHA256: 527d32febfac2d98c1cfc0de8e48c302517dbc286495c25aa0948cc8b25a3b5f SHA1: adb1659afa2bfbdc3c87b504daceb4f75f30cd6a MD5sum: 1a623a27e69dbb3c696531f8f576ec0c Description: convert 3D medical images to DICOM 2D series Nifti2Dicom is a convertion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS. . This package includes the command line tools. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.6-2~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_0.4.6-2~nd13.04+1_all.deb Size: 615168 SHA256: fece278a877eb7992684c0f84f93112a1edbd3d378a624202c80ca8a53216347 SHA1: bd83f73dfe317e221dbd28b4db9479971aae6860 MD5sum: 45a36880d6ed84bbe5c36705b331c45f 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: 0.4.6.2+ds-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1904 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_0.4.6.2+ds-1~nd13.04+1_all.deb Size: 509748 SHA256: ca859a1835a763c9c9644c16b5617e1da607ff56c94f05628f659b553322aefd SHA1: 3ad6d0fdd91d589ef73a24fbf853bd7c0a808798 MD5sum: b23206bb732c19228b4c30be1f1fea18 Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: numdiff Version: 5.8.1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 898 Depends: neurodebian-popularity-contest, libc6 (>= 2.17), dpkg (>= 1.15.4) | install-info Homepage: http://nongnu.org/numdiff/ Priority: extra Section: science Filename: pool/main/n/numdiff/numdiff_5.8.1-1~nd13.04+1_amd64.deb Size: 614666 SHA256: e2e39a8959f3f6a6fc5083ae57fea898ef55c0233c145693126dd4858f16e0f4 SHA1: b02a744ec496e47a19db5cbeeb27a910197266f6 MD5sum: ebee73fbb0ebf3f11f6da28bd778e2cb Description: Compare similar files with numeric fields Numdiff is a console application that can be used to compare putatively similar files line by line and field by field, ignoring small numeric differences or/and different numeric formats. It is similar diff or wdiff, but it is aware of floating point numbers including complex and multi-precision numbers. Numdiff is useful to compare text files containing numerical fields, when testing or doing quality control in scientific computing or in numerical analysis. Package: octave-biosig Source: biosig4c++ Version: 1.4.1-1~nd12.10+1+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: 3.0.11.20131017.dfsg1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2745 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 (>= 2:1.0.9), libx11-6 (>= 2:1.2.99.901), libxfixes3, libxi6 (>= 2:1.2.99.4), libxrandr2 (>= 2:1.2.99.3), libxxf86vm1, psychtoolbox-3-common (= 3.0.11.20131017.dfsg1-1~nd13.04+1), psychtoolbox-3-lib (= 3.0.11.20131017.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_3.0.11.20131017.dfsg1-1~nd13.04+1_amd64.deb Size: 891442 SHA256: 38ce765739c7d203d41b4e63a37ed79ef82e5dd2f5db4ed49055158b320836bb SHA1: b3b3a9d9e48b440d271446dff28fc5f406cbbe50 MD5sum: 46569ab94a50cebc99ad5cee44c4be2b Description: toolbox for vision research -- Octave bindings Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . See also http://www.psychtoolbox.org/UsingPsychtoolboxOnUbuntu for additional information about systems tune-up and initial configuration. . This package contains bindings for Octave. Package: opensesame Version: 0.27.4-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.4-2~nd13.04+1_all.deb Size: 25359352 SHA256: be473efbb1a3c74f86b5d05bd4d841ec3bee3c0e6e61451bd2aa3673834c9c22 SHA1: 1f1e9c48706fb0075bcdce01889f29457ab7efad MD5sum: db4fbffa3d4dc3eb61e2c40cc52fc9a4 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: 3.0.11.20131017.dfsg1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49650 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20131017.dfsg1-1~nd13.04+1_all.deb Size: 19939134 SHA256: 56129b155a6597527f251af2f353c786a92beed15fffdd05ba62ce0761c2b166 SHA1: 62d81cebcf0258bb5caf5211b0bc0d07adda1e44 MD5sum: 5e82bbfae731d0043179fdfa45c0a43d Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: psychtoolbox-3-dbg Source: psychtoolbox-3 Version: 3.0.11.20131017.dfsg1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2699 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.11.20131017.dfsg1-1~nd13.04+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.11.20131017.dfsg1-1~nd13.04+1_amd64.deb Size: 861612 SHA256: f911d8c86646745743aa8eb7acb877a13ec631e8be05ffad5e8ede508d26c4c0 SHA1: 571fce37310bb222db97ccb16f3728ab46cfbd2c MD5sum: 390cb833ef2a8c15f60ffde0abe96cd3 Description: toolbox for vision research -- debug symbols for binaries Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . To ease debugging and troubleshooting this package contains debug symbols for Octave bindings and other binaries. Package: psychtoolbox-3-lib Source: psychtoolbox-3 Version: 3.0.11.20131017.dfsg1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 156 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfontconfig1 (>= 2.9.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.6) Recommends: gstreamer0.10-plugins-base, gstreamer0.10-plugins-good, gstreamer0.10-plugins-bad, gstreamer0.10-plugins-ugly Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.11.20131017.dfsg1-1~nd13.04+1_amd64.deb Size: 65532 SHA256: 32726a0b16247d829fcbb4b9b63e4a1fba262e6fa8193ca9e3ceb2d2b4c6f27c SHA1: d598804725549fa1a0283ab228ab7ae3b4bf1c22 MD5sum: c71b3f3e7a050b42da2b672f335883c6 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: 0.7.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 182 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.7.1-1~nd13.04+1_all.deb Size: 54852 SHA256: 34de23a238a4de92b1b8ccf9459e78ff31134f79260dde329140d39488198105 SHA1: 1ca2b03462493c6e6cbeef719af1bab45c022e93 MD5sum: 258dd84c5aec75e280895604964962c9 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: 0.3.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2485 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_0.3.1-1~nd13.04+1_all.deb Size: 1441916 SHA256: 744b6918fbe2ce203d0154616a00c67d0a5e90f2876b71e88bed02acc6fc985a SHA1: 06a6c04273190807ef947492ff649ff48786b658 MD5sum: 0f97327aa5f6b891fe157fb959ce9f5a 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: 0.12.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5629 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.12.0-1~nd13.04+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.12.0-1~nd13.04+1_all.deb Size: 1080646 SHA256: 79f11d9c552af82969f16ce6d2ab563a6a248d9ecc055669f0d8fdb041ffd5af SHA1: 450be1cf2f562c6e5efcdbc77f63a06a00a7d13d MD5sum: 475d82bca8e5760d4a04b0b9dcf3cd09 Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 2 version. Package: python-pandas-lib Source: pandas Version: 0.12.0-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4185 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_0.12.0-1~nd13.04+1_amd64.deb Size: 1541740 SHA256: ac2091404c53d1727936d993b60507b4e9f7dcc5a4edfce8c762b70044b9eb69 SHA1: 18e1dd291739a85f44fa041f8ed8407dcd3e2bb3 MD5sum: 4afd5f15f02e2b6b7a3fc1ab3fc7d53b Description: low-level implementations and bindings for pandas This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 2 version. Python-Version: 2.7 Package: python-patsy Source: patsy Version: 0.2.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 542 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Recommends: python-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.2.1-2~nd13.04+1_all.deb Size: 141588 SHA256: 4a8f8281baa93259779245035bf908c2aae32c49ca12c18254b711e424f86db1 SHA1: 9183b4262d8853365f88bcb36efa3a536d2e03ce MD5sum: c4224925cc6e1d71fa5b69574ec751d2 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.2.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 827 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.2.1-2~nd13.04+1_all.deb Size: 271954 SHA256: eb3f3e7eefcf670563190265682b6c20ba7fee0bab2da9cf3f07438c99fdba8b SHA1: dc228a83b4e3ca0d0c31fa373d41a8cec4facde6 MD5sum: 6f963190f5954f63d7e18a061f04cd3c Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pyepl Source: pyepl Version: 1.1.0+git12-g365f8e3-2~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-g365f8e3-2~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-g365f8e3-2~nd13.04+1_amd64.deb Size: 381762 SHA256: 0a477f7adade4e35eaf954e44b4e0890bfbd2e63c95d18bf6aad336f4c8a9c64 SHA1: f158faa6c4c4af6c74e22e1624a0c0223b9b6823 MD5sum: 8c4168026575a844a42665531a04d3e3 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides PyEPL for supported versions of Python. Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~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-g365f8e3-2~nd13.04+1_all.deb Size: 818248 SHA256: db03e0e6da1604340b0e261b32b5fbfeb29c5ce61c377a0143c68970b8c84527 SHA1: 66261c89106edb13ad1415cfdc8f980ec0b0b383 MD5sum: 634c2710263e8349bd60a73fd742d18d 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: 0.14.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.14.1-1~nd13.04+1_all.deb Size: 33350 SHA256: 3b26ce343682816c9d20ab5a0ec2951c46ce233a3874719adcd28d08ff32db49 SHA1: dc45e381d04f62a84296a6173bc4a434a6e001fd MD5sum: ff188f7c78cc6f31413834f373b03bad Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.5.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.5.0-1~nd13.04+1_all.deb Size: 5636 SHA256: edd8cc37a13fde303ab919fcf0382e1537107b555ee4063bb0fdf252aefb265f SHA1: 47674665c8a46c2db0e7fec5f3788e0508a769ea MD5sum: 5c856d0a92039b7a0d29eb5a2a90a834 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-skimage Source: skimage Version: 0.8.2-1~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: 0.14.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3549 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-sklearn-lib (>= 0.14.1-1~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_0.14.1-1~nd13.04+1_all.deb Size: 1103558 SHA256: 75ce8de7cb4410c4e7f27119a369af11a2db1bd3501ae92fcd62cb7ebc55eb62 SHA1: 921fd43cc36b78d3894d05c4f18fea9cebbdc974 MD5sum: 32cca45f7d6720d58c8c74e0e106b14a Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Package: python-sklearn-doc Source: scikit-learn Version: 0.14.1-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 579 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.14.1-1~nd13.04+1_all.deb Size: 190030 SHA256: 9cc5098d90f84e0c2b60d6e4edda4253068e584e95289c621c637c879f8186c4 SHA1: 3e2ec356df17a5cf548f3c6d01ff1d43a3efcff3 MD5sum: 3448a9c8ad7df5f8045002acd3af159c Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sklearn-lib Source: scikit-learn Version: 0.14.1-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3856 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.14.1-1~nd13.04+1_amd64.deb Size: 1519146 SHA256: 80a59c06904d39273dac8763200ecf1018c6cebeb525c0faa391f58ef47b9ddf SHA1: 141575a7d7d3d7c2a0e91ef6485964737a41c4cc MD5sum: dd1748e621f2ffd83331228718c6a971 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: 0.4.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2016 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_0.4.0-1~nd13.04+1_all.deb Size: 401166 SHA256: 9a58151d07d7520751a5987409452c390c55f136df28a561ebc5b0b8d9fcd6dc SHA1: 4ef30762d5bb5e78564fc80c848481004ce4c670 MD5sum: a77fecedcbd770e252642a842f578d8b Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.5.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20432 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.5.0-1~nd13.04+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.5.0-1~nd13.04+1_all.deb Size: 4682438 SHA256: 2a21d2b08addd50290ec03381e2ee92c1e7be25e9a5ef0abd907c4416442f933 SHA1: c98e31786fde183d1d5ab82dccd28a1547f61941 MD5sum: bbfda8bb9802b1ab252f1414f429f82a Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.5.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32549 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.5.0-1~nd13.04+1_all.deb Size: 9253286 SHA256: 6fe726032db301674769285750d43dabeadc30ff9771efcc14e2095f0f911177 SHA1: be795b20ee9adfe7fcb982602430c5d89c70e8b7 MD5sum: 2d2d70302b9c703bc34ff802b3d9830b Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-statsmodels-lib Source: statsmodels Version: 0.5.0-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4) Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels-lib_0.5.0-1~nd13.04+1_amd64.deb Size: 105300 SHA256: c39520ee20ba5aa53b4ca743f78841f2f511c7fb69860b0a6db26d8006702503 SHA1: 64adf877a16b9eeec92f2b192e5e2a8b7034738a MD5sum: 0a6a0528a87b045652fb6797ef02fbbc Description: low-level implementations and bindings for statsmodels This package contains architecture dependent extensions for python-statsmodels. Package: python-stfio Source: stimfit Version: 0.13.2-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 825 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7.1-0ubuntu2), python-numpy (>= 1:1.7-0~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_0.13.2-1~nd13.04+1_amd64.deb Size: 273204 SHA256: 0e8899d0403ef2edfd9e59e951f670257399c84b7036af40e663849cf4a82073 SHA1: b15717235739e64a69eb4113cac274d01021427d MD5sum: 89fa7fe1edb5161fb0d6c5bf5d879469 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: 0.12.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5575 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.12.0-1~nd13.04+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.12.0-1~nd13.04+1_all.deb Size: 1076246 SHA256: 1f14eab4938fd82601593dc00d432c489301e1cfba5b01a35f66b02379e4006f SHA1: 6120171eefac9d40f9482cbe83b40381b049d572 MD5sum: b419a7301d33c26d548235a00fccbec7 Description: data structures for "relational" or "labeled" data - Python 3 pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 3 version. Package: python3-pandas-lib Source: pandas Version: 0.12.0-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4077 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_0.12.0-1~nd13.04+1_amd64.deb Size: 1497564 SHA256: f6d10ccef9d7b45a3bce6e7f01d798b00dfd0046f7751c8ad1d4712861123fe4 SHA1: 29eaabae5218037a90e36a7c1f252715b7db5eb0 MD5sum: 2739842306ba04b69843f849c00b0342 Description: low-level implementations and bindings for pandas - Python 3 This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.2.1-2~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 537 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-numpy Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.2.1-2~nd13.04+1_all.deb Size: 140894 SHA256: eb5bfa48cc76fca948b402aef445a9d3a0cdcf2d9f98614cac0c92055ba95fdc SHA1: 0f8d0299ae15ca0caf4b46f1f967eedeaa8c0794 MD5sum: 4cb4170c8a7aaa09a0e878833ec7576b Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: qnifti2dicom Source: nifti2dicom Version: 0.4.6-2~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 (= 0.4.6-2~nd13.04+1), nifti2dicom-data (= 0.4.6-2~nd13.04+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/qnifti2dicom_0.4.6-2~nd13.04+1_amd64.deb Size: 686000 SHA256: 563ffc6d64c83735f320e88353efa59db80685980b0bb434d378e54f5036606d SHA1: f8c2b0f1b771518b07da8653bf64a130e1e406d6 MD5sum: 504e253d8b5e3efa6c7f30a034ab4861 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: 0.4.0-1~nd13.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1120 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.4.0-1~nd13.04+1_all.deb Size: 575216 SHA256: 3496132ea750ffae4ab317e24742bcbc95f7825a42a790c78c809e2aaaf6147b SHA1: 3b3616fd487bf3dba0f2e6b0674203538071d561 MD5sum: 721e9dc215c3810ced07cb174fb3115c 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: 0.13.2-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2577 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.14), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-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_0.13.2-1~nd13.04+1_amd64.deb Size: 847654 SHA256: c097cf55758c299732356c2d99d1071513bc2a838e8141d8e0759c813f914f6c SHA1: 195ef30d740c9f356afec29135bbb22aa8a25f80 MD5sum: 95c6ed5c3c248ebb3b58df862493455a Description: Program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.13.2-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15365 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.13.2-1~nd13.04+1_amd64.deb Size: 4472740 SHA256: a986b2684c279abe67a8f9c7273899aebe7dfa0739b5b43efc558ced1fe835f2 SHA1: d4ae971cce64b5043db6b7449b77c83f093ef4ab MD5sum: be073c68ae2bbc429a8def53f58e31bb 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: 7.3-1~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 (= 7.3-1~nd13.04+1) Suggests: vowpal-wabbit-doc Homepage: http://hunch.net/~vw/ Priority: optional Section: science Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit_7.3-1~nd13.04+1_amd64.deb Size: 20698 SHA256: 725d50dd5d6d46b3e2739684af30435950fbb2713f3348834a503ca513c96b23 SHA1: dc1b5eb771f36aaf2c7af6c1e2206144f3c3f09c MD5sum: 43687c30a7a307327363cec7ebe3f34a Description: fast and scalable online machine learning algorithm Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing Package: vowpal-wabbit-dbg Source: vowpal-wabbit Version: 7.3-1~nd13.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8204 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 7.3-1~nd13.04+1) Homepage: http://hunch.net/~vw/ Priority: extra Section: debug Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-dbg_7.3-1~nd13.04+1_amd64.deb Size: 2397796 SHA256: 32559df857a749afb91cc308afc1c0287b04686a2da2d5da4bdaef4a8ec07c62 SHA1: 059420c271dd022332d4267764a10ec634084f90 MD5sum: 1db09956bcaedd629d39659a44bd9ede Description: fast and scalable online machine learning algorithm - debug files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains debug symbols for the binaries shipped by vowpal-wabbit packages. Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.3-1~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_7.3-1~nd13.04+1_all.deb Size: 50202370 SHA256: 9dddf9b5b7a2237d4f2a93efd545d867f3d204f1d05ab4d462bf15ea4b55e986 SHA1: 8e49788bf4e88ece7e82ef40224840f79f2eee52 MD5sum: 55654e0768d8a01e3e339294bbb06565 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.