Package: condor-doc Source: condor Version: 7.8.6~dfsg.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6162 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.6~dfsg.1-1~nd12.10+1_all.deb Size: 1366096 SHA256: 8e15bde9fcf9281260997edd5b863162eb82d944375d46b34107650250876a9b SHA1: b685be312586eae6b6e7d8e068a62d70c528aac1 MD5sum: 39dd139ebe5243ec2e5bceefa5c4c30d 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: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 7224818 SHA256: 25bbf59e6baaa0fd1f795f650fc89e2fc7f1c9bed1172b1adfe766a6a9b64be4 SHA1: 5b471b69135beae6f699377fdfcb606d1fcb972e MD5sum: dd4f89591443db2aab3bfc912c908f2e Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.8.8-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.8-1~nd12.10+1_all.deb Size: 112574 SHA256: b39e2d62703c7a98c1d93ab02a2471c8218cbfa900f8f0b9737b88ae61bfc992 SHA1: 08d81d3a53315d987b029b367034add6deec0227 MD5sum: 7bdf1426517e7497b5f78341dd127b6b Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd12.04+1+nd12.10+1_all.deb Size: 9726 SHA256: 34850e6858d784f40edaa883e66923b867c1262d92203a3ccde4cd38fc505897 SHA1: efa6a60304adb482d61201f9187f1fb23807d12b MD5sum: 0f86d558162919041ff81fb2e7129410 Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: ipython01x Version: 0.13.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4661 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.1-1~nd12.10+1_all.deb Size: 1285210 SHA256: bb8beddfb1597b99b1b0aca6265c68719008f569a99d81e18219f2cd2d5ea98a SHA1: e71ce397f1fb713cb297b316ce9948f71577866e MD5sum: 9361e87ea7dbd5e412592b53d2d56724 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16635 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.1-1~nd12.10+1_all.deb Size: 7228930 SHA256: 14c74751cb94b2462905f885288e89cf4af430600896da651d8865ca297b5fbe SHA1: 3d34c6126e47be499d5540f0cd067158588b155e MD5sum: a616c5f9c4161a106885b5a7620e3022 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.1-1~nd12.10+1_all.deb Size: 902 SHA256: c7bf09aa5874168543401a85855f9d59b5acf9c3c51ec0099770e49ac99664d5 SHA1: c7749ec14f5a975f6e4805f92c5ffca53ff87e6c MD5sum: 2ab45d60ab4cb38d49f08d5b70858127 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.1-1~nd12.10+1_all.deb Size: 830 SHA256: 4aec6cf4e61e54d4ddb30e57e954ca30a92f9deb606f91a3b418a6cdcf1a7ecc SHA1: 718cf15c2905e382b6a8481f27546abf92e134e7 MD5sum: 82891260957b84a748756fff9b269db9 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.1-1~nd12.10+1_all.deb Size: 912 SHA256: b00958790a164c47d96a48132d70a6a45e05f08fa254ba56408a5b13c999f16e SHA1: 1f9cc05a04a4f641b1ee5688a8562c877d5e7ede MD5sum: bb17a59fe310116281eade9dac09a12d Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: mricron-data Source: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1678 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1_all.deb Size: 1664034 SHA256: 67d11d7a26ee669ce218dec8dedd8de442fcc302031ec0801be4f0a36eaf5428 SHA1: 92e4e8dc8cb2c36ee9f5b889b7596f48d8daadf5 MD5sum: 8fa66eab66da9d13c81f4c66008b472a Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 979 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd12.04+1+nd12.10+1_all.deb Size: 735764 SHA256: adf029ff7c6e162ead06e7cafca311ac7780e43b95dcb97d3f4d5000ab6d4f3e SHA1: ec103ce1298820abe5e9962ed4d42e07dc7cd8d5 MD5sum: f4300f300106bdebf22685ddda9e4078 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.29~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 141 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_0.29~nd12.04+1+nd12.10+1_all.deb Size: 114562 SHA256: 32062fe00ad2ff0b3f8e6a531377bf28b4c0dad0c5ec613e8346566215ca5742 SHA1: e7ae797f8b7f2af5d37adef311914d4d3993d8cb MD5sum: 5c15304c2d12f47d70376e1e8f834739 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: 0.29~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5748 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.29~nd12.04+1+nd12.10+1_all.deb Size: 5346754 SHA256: 406c53d692181cb734d1cf80188366079184148f47c9e581383e7d593a2d18bd SHA1: 0d1bd090825e5c4a0843af30e5433bbaf6b9f54e MD5sum: 34027c8b2eb10238f4efef375f676f0e 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: 0.29~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 106 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | gdm3, update-manager-gnome, update-notifier Recommends: chromium-browser Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.29~nd12.04+1+nd12.10+1_all.deb Size: 14288 SHA256: 866702960f9ed6d2731f3107d84a8db4d52b21f8fd0fe56013e081c0e656c966 SHA1: 6a4fa09c4b33d2a7e0d8445b9bd4867a120003ef MD5sum: 0f8d252eb47e08ba20035796dc1b1aa9 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: 0.29~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.29~nd12.04+1+nd12.10+1_all.deb Size: 6970 SHA256: 85fe1f50f0bc80dba7b7f65502128aba04cf1442ea3b8cea89605ad22c9b317f SHA1: 9a70125a1cca8ff830854548ead84b2987e3b77f MD5sum: ef08f25b0a9624afecdda0d9490dd8cb 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: 0.29~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.29~nd12.04+1+nd12.10+1_all.deb Size: 6140 SHA256: 0fa222006537a1b517ed356efdebe755272476ca9910ec8b43e838eea9ac2bc8 SHA1: fa9cf85cae6ea3d90c86ac5ac31bf85140d2b0ff MD5sum: 60adf40efe4c5d4cf7eecde6101df637 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-data Source: nifti2dicom Version: 0.4.5-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.5-1~nd12.04+1+nd12.10+1_all.deb Size: 615000 SHA256: 272ed3d474a443b383fc5f818890d71d703a6860d58ab6fad247607126fe0442 SHA1: a1e46e31f600d80a861cc6fe5af6a78182b356e6 MD5sum: 61cff1026ec14a6dca81be2aef8d6fe3 Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nuitka Version: 0.3.25+ds-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1377 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.7.1-0ubuntu2) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6) Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.3.25+ds-1~nd12.10+1_all.deb Size: 349442 SHA256: b4323504302b965a3d1a75bc4559bd887afe9cee706212233a4811a4899abca6 SHA1: cfa3e917a94f1194fcb9bb5b1730a0ef3357f06e MD5sum: bbfbbc29b39c82d5a194604c9c2240de Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class to pure Python objects at all. Package: opensesame Version: 0.27-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25055 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27-1~nd12.10+1_all.deb Size: 24027310 SHA256: 7ba92004b82d2b009ad4c8dc25c951f62d73918ad29bfbc17fd9edbc32ac0ffb SHA1: 56cce8453243777b5d702e6d3aa47d33666ba05a MD5sum: 094195802e31e621acacdfa0adc4d836 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: psychopy Version: 1.74.03.dfsg-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5203 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, libavbin0, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.74.03.dfsg-1~nd12.04+1+nd12.10+1_all.deb Size: 3102320 SHA256: 825080ff79bbbd5ac1ea5e58a979acf4e351e753d2e9b38328caea762bf65e18 SHA1: 72f3982ab85940d99481d52b50a98f902131f22e MD5sum: 59edb5e68abec01737402c6ffc8761c8 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: 3.0.9+svn2579.dfsg1-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 47050 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.9+svn2579.dfsg1-1~nd12.04+1+nd12.10+1_all.deb Size: 19434084 SHA256: 8ecf9cd9d55ef7201eda01b7238df269c520192b5caa638dcebb9051fb58cc30 SHA1: d80cc6b6ad02dd9a8519f5ca5dda158f0a8e3522 MD5sum: b466f1367795115bfacf30664b07e9d5 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: python-brian Source: brian Version: 1.4.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2139 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.0-1~nd12.04+1+nd12.10+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.0-1~nd12.04+1+nd12.10+1_all.deb Size: 503240 SHA256: 51357e842efadc5bb97f1842c7b5269fed5e668a6d5d9f89550d2ac2d624ac0a SHA1: d2bb4f2f816f32e4c53b01ce1e336d717daebed8 MD5sum: 18ef5b7b8eb127e34855a44fec92fe1a Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.4.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6133 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.0-1~nd12.04+1+nd12.10+1_all.deb Size: 2179816 SHA256: c3a02bcfbbe6eff889c509b8b261d4c32759d7c5801ef546b02113cd520cd0dc SHA1: 44477ae2af4a058ea08c193390b55a6198ad990e MD5sum: 63863d69005f9f9ab8cee59d40c71fd8 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-dicom Source: pydicom Version: 0.9.7-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1790 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.7-1~nd12.04+1+nd12.10+1_all.deb Size: 419248 SHA256: f3f77a173128d07565fa883296bb7e2e6ac71c2c063503b35dd203af84045cc3 SHA1: 4cb7eb491eb9c51dfffa5af9f9c962df613bc137 MD5sum: 843bc43a1ba02475ab7b61e3ca6ceb07 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-joblib Source: joblib Version: 0.6.5-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 175 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.6.5-1~nd12.04+1+nd12.10+1_all.deb Size: 52690 SHA256: 7cdd6f6998be06124635d64c1993ec41abc7520c83d71a6de17cd104833a6fdc SHA1: 9f19da28d98df6a76983d00eb64b3b6de2f9f8f6 MD5sum: 408d1245807b0d85afdbb3aa835bfc4b Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. Package: python-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1495 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git6-g7bbd889-1~nd12.10+1_all.deb Size: 478636 SHA256: b207ab09eba4efd4f211c30dfcad14fd1d186545f49161e0e577ae0070383bf6 SHA1: 12d82087d31fb3448cfa153cc7f6ca57e64e7272 MD5sum: 528bbc072c4a59d025f381b676c3c6f8 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mpi4py-doc Source: mpi4py Version: 1.3+hg20120611-2~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 284 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3+hg20120611-2~nd12.10+1_all.deb Size: 82524 SHA256: a71a3e30acd4ca335666f96ef827fdca039a1873e8a2dd45f579aed595d561ec SHA1: b3b21af55fab7ce1775ba864328d0023b53af1a2 MD5sum: 305217a25234435dd5ae7761c214dee8 Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa2 Source: pymvpa2 Version: 2.2.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4241 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-1~nd12.04+1+nd12.10+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.2.0-1~nd12.04+1+nd12.10+1_all.deb Size: 2399986 SHA256: e77909b0dccaa44717beab4afe9e83c2c86ff46d9197e562ed27fcca4aca0f22 SHA1: f49f7edfd5c1c294464b7c97cd3243748aa118a9 MD5sum: 7240798423edc4a1d318d8aa34a9d3c0 Description: multivariate pattern analysis with Python v. 2 PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.2.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17215 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.2.0-1~nd12.04+1+nd12.10+1_all.deb Size: 5140662 SHA256: e2632da5ca320dbdd3fab7f532c4fa11990e312979190db16e918ef30dc0e841 SHA1: ba6389d7ed10f3a6bf29eabba44936dbe7bafa6b MD5sum: 505c6dcc506aa9d7bce4a2c8e91bf82c Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neo Source: neo Version: 0.2.0-2~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2181 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.2.0-2~nd12.04+1+nd12.10+1_all.deb Size: 1385368 SHA256: 375ed30dc0a3483275a8a5de9bceb98bba3103aefc5cbf3f2930bba6da785728 SHA1: 5369e5dd2ebd52de4f1de809e3f23cecb7d92fc0 MD5sum: 60a4482c8b65f20f8d05753351d5d5f9 Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4152 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd12.04+1+nd12.10+1_all.deb Size: 1816340 SHA256: f4393634a41ed4334f5833115835ac674f8fd2f0aaac8a8acecaed5d841b37f2 SHA1: 6e07b237d683b17e0807de6d3faaf078698e2968 MD5sum: 3ec51142db5c228429cb67563faa3222 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Python-Version: 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2440 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd12.04+1+nd12.10+1_all.deb Size: 444170 SHA256: 01df549d5c4ea10fc4712a4ee44ba0d4f6eb3ac668043365cd5a1063bcfc7bbf SHA1: 229ecd36ecd5903eafc04e3553ba1609d861e9c1 MD5sum: 84cbf46b773013a504beb30c532bd5b4 Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-pandas Source: pandas Version: 0.9.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3051 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy (>= 1:1.6~), python-dateutil, python-pandas-lib (>= 0.9.1-1~nd12.10+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.9.1-1~nd12.10+1_all.deb Size: 690086 SHA256: efd89585ec6ab659da7d514efa076ec6869e20d8401d50638704fe2c9d884cb5 SHA1: e105c05bcf5ff6e8dce2efdf8393aed6e7bf8fe7 MD5sum: dd9a7bb34e94a576266bda44ea027a71 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 Package: python-pymc-doc Source: pymc Version: 2.2+ds-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1840 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.2+ds-1~nd12.10+1_all.deb Size: 906858 SHA256: 59074e78f8759a1d2cc3f7798cac2ae5c9991ecf4fd266909ef1252e91bfe6fe SHA1: 0cc0b5fb138b9896e1905ba8f80e97b338fab08b MD5sum: 0073ef238c61a59b8ff50c2f2534d15b Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 862 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd12.10+1_all.deb Size: 190338 SHA256: 08b91ebab764e01025c72071be3e9888c3ce9e07099aade433105d3f5a37ed2d SHA1: d441abc798fb076797de9779a6875840cac0347e MD5sum: 39e65e49f17a5ff4237c5009b77bd45b Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-scikits-learn Source: scikit-learn Version: 0.12.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 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_0.12.1-1~nd12.10+1_all.deb Size: 24320 SHA256: 53562647783d0fe36251258f18495a9e939259d0911a6285f13b949bd59fa8c2 SHA1: c9650b75753d8e0c9c15195ddad04b33a3d5d406 MD5sum: b6265933cbb7f828d5b9ed4011c60fd6 Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-skimage Source: skimage Version: 0.7.2-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4389 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.7, python-scipy (>= 0.10), python-skimage-lib (>= 0.7.2-1~nd12.10+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging Suggests: python-skimage-doc, python-opencv Provides: python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.7.2-1~nd12.10+1_all.deb Size: 3155780 SHA256: 392df1b4e8e3870bf6768764aea22cf3b7eb6d3a46f8fa2b4ab3265b4c3d4b1e SHA1: d117de4bc8d07eb8423b352ef1ad4977eb937b03 MD5sum: 3bd131f4fff3ab602af73194d1561e1f Description: Python modules for image processing scikits-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. Package: python-skimage-doc Source: skimage Version: 0.7.2-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8354 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-skimage Homepage: http://scikits-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.7.2-1~nd12.10+1_all.deb Size: 6578200 SHA256: a10af35a7f38140767c659a2402ddd97af358a2408b890320bc33ab40c4ed2c2 SHA1: 31e1208f75da7ed477f45682949fc1d938306663 MD5sum: 714339d017e3e05e32d4c702be97fdf0 Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.12.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2658 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.12.1-1~nd12.10+1) Recommends: python-nose, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.12.1-1~nd12.10+1_all.deb Size: 927428 SHA256: bd77b892fd384ae768929142639cb75386808d7e990f5633cc6f073609c157ae SHA1: 4735f3c5d34cd437ace954e1bbf9295df17f425c MD5sum: c04922c009ff2b3f86a73b7214eba32d Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.7 Package: python-sklearn-doc Source: scikit-learn Version: 0.12.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36628 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_0.12.1-1~nd12.10+1_all.deb Size: 26700106 SHA256: 06b2598b3858652b552cf0b9a924a3c08b1a06f5e33171a20a6a365dcaa8dbb3 SHA1: b653e46c1102a100e1a6b0f26d54743b2bec31ec MD5sum: 89edff9adee18d7d86fd350e22137310 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd12.04+1+nd12.10+1_all.deb Size: 28082 SHA256: 2a0d8f7bea7b8e7fbe80619282bcd1c0b6874fbc2569c3248451c752f1cdc4dc SHA1: 186db3b9114826618485059e3582945a132d76f5 MD5sum: 7cc577897180b73015b7f99b17c6d04f Description: visualize Freesurfer's data in Python This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. . PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets. Python-Version: 2.7 Package: python-tz Version: 2012c-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 114 Depends: neurodebian-popularity-contest, tzdata, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd12.10+1_all.deb Size: 39078 SHA256: e512dd91a25410d50f9579997ac91ad0112084db3472a4d52ecd2bd4294453d9 SHA1: c84153071fa6e5b7565e216b0312f0ce5c7e5806 MD5sum: ac19c9c5c33c317608e638b9a35d9a32 Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Package: python3-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1457 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-numpy Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python3-mdp_3.3+git6-g7bbd889-1~nd12.10+1_all.deb Size: 472468 SHA256: 89e6aaa24e0466c077008ab49d9ee6f262e515e0fdc500f760852be9080d763a SHA1: c64c2e32bb873012649d600340e60dee84346879 MD5sum: f82a99a09536a7744d28e6d286e85450 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 3. Package: python3-tz Source: python-tz Version: 2012c-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Depends: neurodebian-popularity-contest, tzdata, python3 (>= 3.2.3-3~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd12.10+1_all.deb Size: 31094 SHA256: 541debafe90874ce85aa69a2e53d7ada2801158b6f51a6d77c5b53e1555133d5 SHA1: 97f75a58b3b9eecf005f3978a69ec811f3d14c1d MD5sum: bc8c7ae5fb1cfdcc84fd641c71adbdbb Description: Python3 version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). . This package contains the Python 3 version of the library. Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 18467 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4667~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 10573812 SHA256: 8eeb69c28d8bc812d050a03f643ef47fd9ca61da620c2dce61916c1589f981d5 SHA1: 7c3aeb64beb10f554efbb6f00795fbb7b8edb359 MD5sum: aaabf7bc6f7bdc4d8b09c4f3637bc671 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: 8.4667~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 72987 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 52167766 SHA256: 97af636dd454562917c7776bf29eeda02133b789f72ca0c1574c0b69c34a84ce SHA1: 0bcb986496f0f413dba9b18e9c7b40c59486bf5f MD5sum: d9298224fbbc0496778f68f83eb5d24b 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: 8.4667~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9242 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 8990964 SHA256: de8e156a6572ef9234441722cd628b7d0a2f541deb3a7e1875f7c0bf9c73c885 SHA1: 50b16592314302803dfa31ac6ca03d2bed203b50 MD5sum: f040994ba7e7c8c9f4a211e19ab683f2 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: stabilitycalc Version: 0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 28774 SHA256: 49039b7b76aa244e4ab34fb04efe43f167aa10e762799ff318276089bf7c2acf SHA1: f2a5e4c70779898ef2164710d40febc1320a6116 MD5sum: 03a808a4acccdd5a48c6b8d10f8b96e5 Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.7 Package: testkraut Version: 0.0.1-1~nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd12.10+1_all.deb Size: 102648 SHA256: ea9a0dc6202062ce41b66a99c7636103bbfa784f108edb7e3a3a0ca6eee285bb SHA1: a2ebf2e13a47bb725d76a53f880fb5ca9d4e8abd MD5sum: 375ecec2bf2a9be209520da130d2c74e Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.7