Package: condor-doc Source: condor Version: 7.7.6~dfsg.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5346 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.7.6~dfsg.1-1~nd11.10+1+nd12.04+1_all.deb Size: 1301710 SHA256: 88657339844f8d354f425375821774baf65887e64a8b186bf85b1ec213ed973a SHA1: 7c7de523e50140f9ed4e72aff264d87649d0c3e0 MD5sum: 1e7a74696e9e07be6453936c657c5f21 Description: documentation for Condor 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 system, 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: coop-computing-tools-doc Source: cctools Version: 3.4.2-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2319 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd11.10+1+nd12.04+1_all.deb Size: 310964 SHA256: a39fa9bc2251d5a045a6126278b1e772f41883e8ef1dd939b2b2c903df0fe40a SHA1: 79255768567dff8808dd954c9e64dc6c99dfd89c MD5sum: 3b540998ed6b8eda89421dd11cd61d6a Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1_all.deb Size: 7224748 SHA256: 896e3a64a84c1ecfa3f8aeb72849dbad8afb923046a6efb8f85cef680dd88880 SHA1: 4d6ca1909de2b2ec07ad124633c1ea2a0e41806c MD5sum: 66c6e6dde6068a39cf8a541bd1b66848 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: guacamole Version: 0.5.0-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 255 Depends: neurodebian-popularity-contest, guacd (>= 0.5), guacd (<< 0.6) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.5.0-1~nd11.10+1+nd12.04+1_all.deb Size: 234240 SHA256: 5c28772cf3c60990043996002c9c35216f6ffd59a48fd66735977f33d1df34f6 SHA1: 28b9a747186f78d6f48ef681a641e1ae868cbf53 MD5sum: 61b270f34c9ee9bab7587db0cf4d0586 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole Version: 0.5.0-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.5.0-1~nd11.10+1+nd12.04+1_all.deb Size: 4990 SHA256: 850ddeadaa54768cda09eda402c0a30ba867b8d37d60a5cfe033a689954c894f SHA1: ce489443324f5d13b7ebad02218135f5b996fd27 MD5sum: 4a28115a5c646fd063e4a10f108521f8 Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: ipython01x Version: 0.12-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3351 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-zmq, python-matplotlib Suggests: ipython01x-doc, ipython01x-parallel, ipython01x-qtconsole, 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_0.12-1~nd11.10+1+nd12.04+1_all.deb Size: 913502 SHA256: d67dfaa66d15d1aea36c251992dc6a8c2487fcaa8a2904e96bd5be0681523219 SHA1: 007eabf1000538cd9b2214dd7707e0ca00cb3ad5 MD5sum: 2b0dc08460e78236e43200889cb9a6c9 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 workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.12-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12919 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.12-1~nd11.10+1+nd12.04+1_all.deb Size: 4495376 SHA256: 925e4dc272a06325dd6725695291ff6c6409bb6f347adce60d021576978169dd SHA1: 43e32ca15d97eba023128596c23789e0a084e807 MD5sum: bc40e9a9c3c8c4a95dadd6b788747c46 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-parallel Source: ipython01x Version: 0.12-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 495 Depends: neurodebian-popularity-contest, ipython01x (= 0.12-1~nd11.10+1+nd12.04+1), python-zmq (>= 2.1.4), python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) 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-parallel_0.12-1~nd11.10+1+nd12.04+1_all.deb Size: 115490 SHA256: d63a51a37a982c4bc51e6eeac478cf277fc0d6760d55ebbfd743e2549f238391 SHA1: efa76fbd9b792a2a8c4ad37653c1ac63b8b93875 MD5sum: c9f1f72098018068d178aa7c616d454b 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 parallel processing facilities. . 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-qtconsole Source: ipython01x Version: 0.12-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 Depends: neurodebian-popularity-contest, ipython01x (= 0.12-1~nd11.10+1+nd12.04+1), python-pygments, python-qt4, python-zmq (>= 2.0.10.1), python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.12-1~nd11.10+1+nd12.04+1_all.deb Size: 79940 SHA256: 0606f485616ee56ba5f259328c41ae2f6998dd9ce7770d6b61a6541dc40c6324 SHA1: fb7e9382647f810f1b64aebb3645b7363a589a77 MD5sum: 17753a24e04b3dd93b2b3c49d728e684 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 qt console. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-5~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 482 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-5~nd11.10+1+nd12.04+1_all.deb Size: 90736 SHA256: f0a22b2d1e7f6bc4ab6e8e8a2739510c37cc23afa20e77dcdc4596782b581769 SHA1: 52aa42fa0fb270a7d96042b3af828172ae62df1a MD5sum: 8b785c406fd5a7b0edb2aeaabb5c584a Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libisis-core-dev Source: isis Version: 0.4.7-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libisis-core0 (>= 0.4.7-1~nd11.10+1+nd12.04+1), libisis-core0 (<< 0.4.7-1~nd11.10+1+nd12.04+1.1~) Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-core-dev_0.4.7-1~nd11.10+1+nd12.04+1_all.deb Size: 69044 SHA256: 676aa659129766e70ed2a833d4341b47e95766cffe931f88ed304d34e700696e SHA1: d8c229966b0716f28ff127aa29d3909149fc1aa9 MD5sum: 8b63e70f78d6b4906a79298a3cb3d11a Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides headers and library to develop applications with ISIS. Package: libisis-qt4-dev Source: isis Version: 0.4.7-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Depends: neurodebian-popularity-contest, libisis-qt4-0 (>= 0.4.7-1~nd11.10+1+nd12.04+1), libisis-qt4-0 (<< 0.4.7-1~nd11.10+1+nd12.04+1.1~), libqt4-dev Conflicts: isis-qt4-dev Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-qt4-dev_0.4.7-1~nd11.10+1+nd12.04+1_all.deb Size: 6062 SHA256: d421f9a584c148a1d0506dc3fe98ef26ded298ba20cdd7852f4800209078f8da SHA1: 4b1fa88ac55f52e9b6bb1531467fb40c6c6ee386 MD5sum: ff41be8c4a67e54cd9b0c8427491a549 Description: Qt4 bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libvia-doc Source: via Version: 2.0.4-2~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 903 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd11.10+1+nd12.04+1_all.deb Size: 118526 SHA256: 70f7d7d0530cd4a253c866ab3a50af8055d66a646e80aeb59d04c02a6c8f3766 SHA1: 80e49482b9207ed42f89c6b98dc5ea1ba7ff8cae MD5sum: c5bb11d4da40e97b8af9c9577f74be1b Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: mrtrix-doc Source: mrtrix Version: 0.2.10-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3485 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.10-1~nd11.10+1+nd12.04+1_all.deb Size: 3315494 SHA256: 94dbee8905712de4c824ee39eb7a83e3cd44eeebc0e39c601a94222acae7d6ef SHA1: 43dd82edbe0141bbe64ba8911db7d23760243a3e MD5sum: 8829e0008103ae5a0eb17ad53039ac26 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.27~nd11.10+1+nd12.04+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_0.27~nd11.10+1+nd12.04+1_all.deb Size: 113984 SHA256: a6259db1886bf2391999c315fe87f1ad92b8f15a3748ac1936f708897a6f1055 SHA1: 88f35f3d28f83ca6ee8f7c5889568d7e4ac94c70 MD5sum: 7e5ae55698d0494fee38a774b990a90b 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: 0.27~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5451 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.27~nd11.10+1+nd12.04+1_all.deb Size: 5087752 SHA256: c7681bf87794e6605db968fe1467f305f2cfd0ca697d95e409f238302353317c SHA1: 135b75c0a2ba1a5d2fd9d791146e9128d74a1753 MD5sum: 0b83a74155e79154284a3a51cbebd793 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: 0.27~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 105 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm, 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_0.27~nd11.10+1+nd12.04+1_all.deb Size: 13482 SHA256: 63d6f354450845680564e7d2b2fd261a50acd74668dcd2cb84f564f22c038485 SHA1: 5a97c7ab889826ba05ae0a9ceab2e6d64b105eb0 MD5sum: ba1de5964bdf4bc1636e1b6d0e91f41f 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: 0.27~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.27~nd11.10+1+nd12.04+1_all.deb Size: 6438 SHA256: 4d280247d31486c6c7c4debfa18799da567c9a040d9da8f18bc3a9b4a151a265 SHA1: 1d2fdceacff0b06ca0ba05ae44a8e01e178e489a MD5sum: 51cc71d1405e20035e1279436b871607 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: 0.27~nd11.10+1+nd12.04+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_0.27~nd11.10+1+nd12.04+1_all.deb Size: 5602 SHA256: f26a819cd9d593a68621b8a4d4e5ac178a45a4172283b4340e3a5a4be26efe26 SHA1: d2ae90771f8d4622b6a940d032ee880f9cdba4c4 MD5sum: aeaa0c63081a4a8ac55d92a64ac12e0a 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: nuitka Version: 0.3.21+ds-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1330 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5, 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_0.3.21+ds-1~nd11.10+1+nd12.04+1_all.deb Size: 312932 SHA256: fe728f3c9203f358c1e4b3797ee35107e2b7fe9e0431518c3fb33119dad8c3c5 SHA1: e68bf3b8ba4c3c1c6501ee5508998729923f1c46 MD5sum: aba63422bbf36ea8af6c560a498da5ec 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: 0.25-1~nd11.10+1+nd12.04+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_all.deb Size: 2839194 SHA256: e8d919bc2638e4d67161cdf6e133c2fc24295507ab78c5f794123be5cffb9d49 SHA1: 1d520aaff6020a467e05a6fe8df780d78cfd9277 MD5sum: 7289afa07f6168567b8006cc48daf7f7 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: packaging-tutorial Version: 0.5~nd+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1485 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.5~nd+1+nd12.04+1_all.deb Size: 1115438 SHA256: 94f0f1d5a6de25f02b91aaa4ac6bc6361b0e07746a5a167a449269306473f0f5 SHA1: 10b9a27e249b989d6e2fba44b13a089842881e05 MD5sum: 875896d8fa600885036bf900319303c2 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.73.05.dfsg-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4454 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_1.73.05.dfsg-1~nd11.10+1+nd12.04+1_all.deb Size: 2686212 SHA256: 51c9864a96d39e84cc99d6c1e293a457b271155e6868b583182299ca2e78586d SHA1: 71ccb74a2a76f87ab1dbd51fd44789e4718b0d01 MD5sum: 84cc95cd5f17aa8d326554288ff35ece 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+svn2539.dfsg1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 47031 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+svn2539.dfsg1-1~nd11.10+1+nd12.04+1_all.deb Size: 19425378 SHA256: a9feb92d2f40fd4e86f7787809e0e8978081a9e11c43f078a37e7d106b199a33 SHA1: 8b47320dccb157dff9f9723dd55586931da12f76 MD5sum: c481bb2b71777879e413ed020546c296 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-joblib Source: joblib Version: 0.6.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 169 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_0.6.1-1~nd11.10+1+nd12.04+1_all.deb Size: 50830 SHA256: cd26a7b2c664253946dd1edf1a10edb4fdeb4a3632834d95fe747f1989d76200 SHA1: 626d83c297770ddc4f79167a4ae41a54ec1bea50 MD5sum: b39858389007d1d80d6a0f5dc891dafa 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-lazyarray Source: lazyarray Version: 0.1.0-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd11.10+1+nd12.04+1_all.deb Size: 7346 SHA256: 119a709e7c8d3e6452027994781665214ff7df777b409d2ecfbbbf805c1c6240 SHA1: ca46afca4f6d4e692a37427d5a85d3eb2f6f2c86 MD5sum: 69478e3c00645627a84e5b965942f006 Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.2+git78-g7db3c50-3~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1490 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_3.2+git78-g7db3c50-3~nd11.10+1+nd12.04+1_all.deb Size: 476414 SHA256: e5dcde45023b37eb570a71ed0cb37771857184365c41f6bc0b619485bdf45d98 SHA1: 8040bfdbe02cff723ffbbce67dbb5ced3bc0e167 MD5sum: 50ebaf4014a55ec6932dbc3a7e4d9b61 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-mvpa Source: pymvpa Version: 0.4.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd11.10+1+nd12.04+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd11.10+1+nd12.04+1_all.deb Size: 2205054 SHA256: 157565eb22e6a64cca8e7b9369dccf884a44fd2b483184b2f9740d4818cc3f3d SHA1: 522691bdde24041e16a7feadd234cc1866586da4 MD5sum: 2f14582aa4fdd1736736b78e7026ef43 Description: multivariate pattern analysis with Python 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, GNB, 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. Python-Version: 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37578 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd11.10+1+nd12.04+1_all.deb Size: 8480396 SHA256: 0b362f8c219e02d176900b865bc51b26b54f6350eacf1d66bcae93a48b3415ff SHA1: eaafef89dd957e059ad348d1038e565c8ecf0db8 MD5sum: 0bee5fc34f30a40bb7a1e3b88f187d5b Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa2 Source: pymvpa2 Version: 2.0.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4023 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.0.1-1~nd11.10+1+nd12.04+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py, python-psutil Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy2, python-mvpa2-doc, python-sklearn Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.0.1-1~nd11.10+1+nd12.04+1_all.deb Size: 2334172 SHA256: ef6772cf5e02a2933fa855214793ad994144c86c04023d67827ef7aedd4f2c29 SHA1: 172892896cd6a44a9fe7e6bac419b44c83598995 MD5sum: 0c571bbe7e40646f684d750dedf99332 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: 2.0.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15373 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.0.1-1~nd11.10+1+nd12.04+1_all.deb Size: 4532148 SHA256: 16422349ffc5e5f380a1870d4835634864594274a40ed6d44a5bf71092361e9d SHA1: 8583e175aae6fb82375d21ddafd0b2744e7cc1b3 MD5sum: fafb4973ee2c8d18c816a0e6089bd834 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.) as well as example scripts. Package: python-neo Source: neo Version: 0.2.0-1~nd11.10+1+nd12.04+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_0.2.0-1~nd11.10+1+nd12.04+1_all.deb Size: 1381540 SHA256: 7b5dc7ee7b390dbd5cb05f6ef5c0ee0b7a66019ddac12fc9d9b4a28b1747cbe0 SHA1: f27d2bcd52e0381fd8f5b45e133efc490f638816 MD5sum: 2f00ad8a38412ad8a1e5951a4b5eda50 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-nipype Source: nipype Version: 0.5.3-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2245 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.5.3-1~nd11.10+1+nd12.04+1_all.deb Size: 501260 SHA256: 8c1546df5a4dd36f4a28fc276e0c11487a137fe4a4a489d4137615ea826ab3b8 SHA1: c8057318888e0af5a67978605ac6390611dc21d4 MD5sum: 6d30266bb7baa01abb4179430c5bcd81 Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.5.3-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12137 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.5.3-1~nd11.10+1+nd12.04+1_all.deb Size: 5744988 SHA256: d79b47e899a5f7a847cf72770a1412a8d9718bc4192ad387f3ee83e260f51c79 SHA1: 01a52fb96c5b418164558ddcd65ab8520776137a MD5sum: 1f106f7fb75301d2bf7a4023bcab47a9 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-openpyxl Source: openpyxl Version: 1.5.8-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 356 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.5.8-1~nd11.10+1+nd12.04+1_all.deb Size: 71670 SHA256: 7d0fe39de7b9a4f5b0f452b1cc659b330611eb2cab4dbc53c228c6672dc53266 SHA1: b0ebf399607650c01aaea0edd98913478500eda1 MD5sum: 9fb7775a21b0a29f6c58a3bd02787427 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pandas Source: pandas Version: 0.7.3-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1921 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-dateutil, python-pandas-lib (>= 0.7.3-1~nd11.10+1+nd12.04+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.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_0.7.3-1~nd11.10+1+nd12.04+1_all.deb Size: 460948 SHA256: 36cc272e134cee6da1490b4c5f02ad0ede868acf870c6b9edcea72233f519a2e SHA1: cb8decdbe1c7494ac0824ee84f7aa491125ef79a MD5sum: b65e810adf909fac49049cc4f6d51875 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-quantities Version: 0.10.1-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 301 Depends: neurodebian-popularity-contest, python2.7, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd11.10+1+nd12.04+1_all.deb Size: 58804 SHA256: e8cc2d0a4d86512648fb8593ef8dbc22e198d5a23dba4290c2fad574a1705185 SHA1: db73d2cfddb1e9b6e19e5f8d674d94cb8b5f10b3 MD5sum: 34ca36fdfe957727bfb6967fddc589f5 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported. Package: python-scikits.statsmodels Source: statsmodels Version: 0.3.1-4~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12181 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Conflicts: python-scikits-statsmodels Replaces: python-scikits-statsmodels Provides: python2.7-scikits.statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.3.1-4~nd11.10+1+nd12.04+1_all.deb Size: 3094854 SHA256: 5e4861df4d97608a0751756abc5a9ba456737b6abc1cbd79eedf16ec26c0f718 SHA1: c36501b240dda343e3f751661002d0fee15c3f1f MD5sum: 42e52b74221b27bd638704219d752b62 Description: classes and functions for the estimation of statistical models scikits.statsmodels is a pure Python package that provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Python-Version: 2.7 Package: python-scikits.statsmodels-doc Source: statsmodels Version: 0.3.1-4~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15099 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits.statsmodels Conflicts: python-scikits-statsmodels-doc Replaces: python-scikits-statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-scikits.statsmodels-doc_0.3.1-4~nd11.10+1+nd12.04+1_all.deb Size: 1902080 SHA256: 87ee6a09bddb246fd587b66220a660f0cd95a6d71b67c81df6df82614ca63c18 SHA1: 2eb8360dc1fbd3155eaf05be5fe08624036ba0dc MD5sum: ac6dbb4753f548add0bb878e62d6d087 Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: python-surfer Source: pysurfer Version: 0.2+git29-g3a98681-1~nd11.10+1+nd12.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 87 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.2+git29-g3a98681-1~nd11.10+1+nd12.04+1_all.deb Size: 25652 SHA256: 6a3fe0bbdfad20185c9f215df11a3b333e977b89ca3c58e17046c6671be94b2a SHA1: b2f44e2414c770da2d9a41fcbb5f07443d6920d2 MD5sum: 55d0f76fe3e7b5ce10e814937b3f6146 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 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_all.deb Size: 10573752 SHA256: eedaff94855047442b4e87a150ee5790c4f85cee4944a31f374fddaf09c5d135 SHA1: 38cd5b25a48b8f7e59ffae64c289e240efdcd675 MD5sum: 9ff1b4420be237904ddbcdc6baa39b12 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 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_all.deb Size: 52167750 SHA256: d35056af43e554c2fb498839cdc2879b41b3e9cb3a17ebb7cee908b41003f47a SHA1: 01a7758933f7769692dd266201019b0c99fbf68d MD5sum: c904d656f804c259bca71a6a7b351375 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9370 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_all.deb Size: 8649042 SHA256: e0b1b632ef8dd21c9f400996da1042d8d3af9cf58b04c9f9ffaeb8e43b232e51 SHA1: abd1d4462abbf95b4e2691c7e44584b0ebcb8a55 MD5sum: 422eb0c57e44691ac1af685fcaecf77f 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 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_all.deb Size: 28730 SHA256: add473af6d9eb0497a244d721862483deeb0fd562cabf84305eefa9e9c522897 SHA1: f6952357804556ee3b33d3242d205b4aa3cc49c7 MD5sum: 472471b057239fd1029854d1f42c735e 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