Package: condor-doc Source: condor Version: 7.8.2~dfsg.1-2~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6100 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.2~dfsg.1-2~nd12.04+1+nd12.10+1_all.deb Size: 1331694 SHA256: 68ed3baf3c6bc816c394bf256fcd5d959bd90fdbff69ff1b7d210e251e81cf2c SHA1: 3bd5c1a374441c2ca2943da211d2d1773c78e1a5 MD5sum: e8566059e040c19e20bad824bfa767fe 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: 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~git33-gcfc5692-2~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4660 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~git33-gcfc5692-2~nd12.04+1+nd12.10+1_all.deb Size: 1284736 SHA256: b33fe2d592d01644998eb2e3bc890c25603a29cce31206f6f29932415951133f SHA1: 3f4113a83607a389037683969e8caade00a5d669 MD5sum: 473207982d79acf9947e95404ea568d8 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~git33-gcfc5692-2~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16630 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~git33-gcfc5692-2~nd12.04+1+nd12.10+1_all.deb Size: 7229016 SHA256: 881a444ee2c6d3503358921aaef312a447c836fa73801a27243f5176d13fe5c1 SHA1: 270cbd0fcd103cb0c6d84fcebda1de3932c7865d MD5sum: 444ff6826d670d1fd6f0c4e9589c8109 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~git33-gcfc5692-2~nd12.04+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~git33-gcfc5692-2~nd12.04+1+nd12.10+1_all.deb Size: 902 SHA256: e419ba065e85ef22cd296029af98564adf14a9332999c17c1132e7f5fc77b330 SHA1: de4ee34412823e15deafb6e01dc025ae17acaccf MD5sum: 57ba052dcaa4a910f24812c728bf2cf6 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~git33-gcfc5692-2~nd12.04+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~git33-gcfc5692-2~nd12.04+1+nd12.10+1_all.deb Size: 832 SHA256: 8f7ffeea044193aff74e58faeb12638ee38d92ce5ffa60433676e6deb1139c4a SHA1: cb740b034905cd12baae6ff52893a8d607438878 MD5sum: 1d2333cd5d2fa1b0c083d93ae024d72f 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~git33-gcfc5692-2~nd12.04+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~git33-gcfc5692-2~nd12.04+1+nd12.10+1_all.deb Size: 914 SHA256: 59433b7f7b77e8489e1af4627b2450943ea10ca88105a27835e997d87a450b04 SHA1: b3ba1be85906ebf378047d8c15dae71f8903c66d MD5sum: 79d9cb7a8cf067f2ecd01a9bb5155d52 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: opensesame Version: 0.25-1~nd11.10+1+nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4136 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 Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.25-1~nd11.10+1+nd12.04+1+nd12.10+1_all.deb Size: 2839210 SHA256: 6ade3c8a844117b6969e4109d786d88a82909f659ef3734b82de0048c4293c03 SHA1: 6d74ddaeaf8a74342ec4de39244cf5564860a66e MD5sum: 1ee444df1bc05108060f211f466a91dd 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. Python-Version: 2.7 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-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: 0.9.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2948 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy (>= 1:1.6~), python-dateutil, python-pandas-lib (>= 0.9.0-1~nd12.04+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_0.9.0-1~nd12.04+1+nd12.10+1_all.deb Size: 663392 SHA256: fc7c11fc75b37ad043d25f1af3423ba8abf9c77f12406539839ac2ab19cb5849 SHA1: 73359968ee75b6c0b1efbed83d1cd06a37c78d30 MD5sum: 2f6aeecd4b5db2d04d028659ba8ad309 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-scikits-learn Source: scikit-learn Version: 0.12.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26 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_0.12.0-1~nd12.04+1+nd12.10+1_all.deb Size: 23956 SHA256: 55a956711deb3ecde4a19b4c5298bfb9326fa8d145ed8b2f0950c28d99596ba6 SHA1: 1a00af5ffcbfded69b51e15de4bd4a8fdfc3ded3 MD5sum: 3c3e5205e76fd67aeeffd02d0fb01eff 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-sklearn Source: scikit-learn Version: 0.12.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2648 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.12.0-1~nd12.04+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_0.12.0-1~nd12.04+1+nd12.10+1_all.deb Size: 924398 SHA256: 88e27d68555bc394efbdc4690c87b4bd622d238573d26dd7273a286691820c44 SHA1: ba3ec82a8519fcb78f7de748c42b585f604cc025 MD5sum: 52543735b773c8a0a2ed5aa9ec01cdd1 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: 0.12.0-1~nd12.04+1+nd12.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36621 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_0.12.0-1~nd12.04+1+nd12.10+1_all.deb Size: 26688374 SHA256: 7c84a8d7717f0a79f18dbd8ed4f375de8a39dfef421dea0d0babd7b1475d1c1a SHA1: 18378ecce2446c8f7507aa5c5a2c16e9b277da2f MD5sum: 8acb69766a99816dc1b5c4114223b67d 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: 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