Package: condor-doc Source: condor Version: 7.8.1~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6932 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.1~dfsg.1-1~nd11.10+1_all.deb Size: 1327260 SHA256: b20afbd1c54b41c1d18f481f83332cc29bb500ea190a99aaab559dd5dea43432 SHA1: e823670ae2b16fe5ab93359d9571f10e891bc4e2 MD5sum: 4e5913e98362ebdd5f4ed1251db0d277 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: connectomeviewer Version: 2.1.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1888 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2 (>= 4.0.0), ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.1.0-1~nd11.10+1_all.deb Size: 1355528 SHA256: e9b4a0a11015223cc11a4584628a4311ab652d5bdab4fc48fcf41be90bbbbd4d SHA1: e4af53fb990202ac3fc96db4e6945862682dc33d MD5sum: 54d65738ed41265fa85a9e51dfdd60e1 Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: fail2ban Version: 0.8.6-3~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 612 Depends: neurodebian-popularity-contest, python (>= 2.4), python-central (>= 0.6.11), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-gamin Suggests: mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.6-3~nd11.10+1_all.deb Size: 103454 SHA256: f0c8f860708a30f13a5dced8fdcd1633e9dd31d0f0d5508520393e6b90976e1c SHA1: 7f79a01d8470e34f8638513ee3be3eb8e69f4ba2 MD5sum: 398cd3a1faa72db92f156eb70a617810 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Python-Version: current, >= 2.4 Package: guacamole Version: 0.6.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 348 Depends: neurodebian-popularity-contest, guacd (>= 0.6), guacd (<< 0.7) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.6.0-1~nd11.10+1_all.deb Size: 277290 SHA256: 3c5f20224ce82075dfb4b8fb6aa54ab2f880fb7090f21364aae21558c15e4369 SHA1: 7e28d55421f0b391410a7740e79d53ea63402b3c MD5sum: c7e929aeb3b79b891f62b82758d24d04 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: 0.6.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 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_0.6.0-1~nd11.10+1_all.deb Size: 5176 SHA256: e0d25a3269c7106d2195c7143a2d8766b29c4f7b2d297cc6a54fad52025dd12c SHA1: e83b6aed27cf78415d9c62a2c3961017902697ee MD5sum: 7f06977609c49671a7890dc2af525b3e 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: 0.12.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4988 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.7 | python2.6, 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_0.12.1-1~nd11.10+1_all.deb Size: 960440 SHA256: 649a9875ba7ec51c644aa5a409ea842f707a263c3ea00ba2740679f780d7949f SHA1: 3fdd066eabb7d51c80dd53c5349ba61ce856e7cd MD5sum: b84e5c48a2e1d21e954b8095f4b86648 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: 0.12.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13820 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.12.1-1~nd11.10+1_all.deb Size: 4307094 SHA256: 2dbfc93180d64e51a38ee024a368ad4ecf49fe19953db7dec0e9de729d19edd3 SHA1: d94af37997509b0e5e1e13e1d81cb34fd9c2a2f9 MD5sum: 19a5e0a69ac6204164054654912bb82e 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: 0.12.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 624 Depends: neurodebian-popularity-contest, ipython01x (= 0.12.1-1~nd11.10+1), python-zmq (>= 2.1.4), python2.7 | python2.6, 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_0.12.1-1~nd11.10+1_all.deb Size: 116952 SHA256: cfb7880b1f155a3a5f378e23fab74db53d98ddfd8516ac669657abe121ed8c2a SHA1: 04b3854f21fce5c81700ff9956236dde0b29b7aa MD5sum: be059075e26983596a21785e34cba47f 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: 0.12.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 396 Depends: neurodebian-popularity-contest, ipython01x (= 0.12.1-1~nd11.10+1), python-pygments, python-qt4, python-zmq (>= 2.0.10.1), python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.12.1-1~nd11.10+1_all.deb Size: 80526 SHA256: 8f0d01dd03b0b035f2db885a6fb8e64f0a480b32fe3368c5581df4c4bc3268e6 SHA1: 6ec2f974487af2da37dbdaecab6c6f41e031e568 MD5sum: cf60dc0253129242afe3f2f61285e3e9 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-6~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 596 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd11.10+1_all.deb Size: 89476 SHA256: 8e365d03a4f610d00bfa85305eebd9ed2868992b74385789ed861527557baa09 SHA1: 8f1cfb43a9d6f9dabcfaf55ee397bcd0d267dd43 MD5sum: bf516848c37dc6abd53603b7a5806d9c 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: mricron-data Source: mricron Version: 0.20120505.1~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1804 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~nd11.10+1_all.deb Size: 1663990 SHA256: 57a67faa032a7cbb1a1d8d9295e971f4e10662b6c85e7730ae330927f2418aa2 SHA1: ef1ce70993c8aa4b1936b67351b4c86bc02c1683 MD5sum: bf04d2e9b077d9d4a172661f23de9516 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~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1176 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~nd11.10+1_all.deb Size: 735712 SHA256: d81d7ef15eb924b87d587143c266c148d2a809949aee74e7fd57c1a1eaec4820 SHA1: 55d141c48923b332cc98a230cf6ca324e063b7e8 MD5sum: aafbd101ef1573edbe13722f3dcb96ba 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: 0.28~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 268 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_0.28~nd11.10+1_all.deb Size: 114140 SHA256: 596b4ba74ce2344dc08294ca9daf719073a6fda26db7aa7e3a8a3cdc6056ad86 SHA1: bb5776b805862ec353cffeeae375c87913e72d57 MD5sum: ae6f0677b1dcb19c7a70c25f4e372198 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: 0.28~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6184 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_0.28~nd11.10+1_all.deb Size: 5345858 SHA256: 0f66c38762bd0d5923d8805b59fc9a1b0f674a13f00c294cacc020fcce87057f SHA1: f7c11781dfe0e20b946ebec9e5db394f51970c71 MD5sum: af500470eec557dbd192d4cfde2c8a91 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: 0.28~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 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_0.28~nd11.10+1_all.deb Size: 13642 SHA256: a2fd8fee6b9525534e10003e30f8c2bfef6e5382869f4a38cdfb79658c971091 SHA1: 719c7b2ed1e3f428cce02b3097f1d8bb6c4d8113 MD5sum: 5cc7feceb0e07ab06b95be15794d2070 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: 0.28~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.28~nd11.10+1_all.deb Size: 6570 SHA256: 8515e7107184fcfec11dac4a7ed0719339201f69417f866953e163b4cfaf6dca SHA1: b1e3c662db0c8cae28d9697585f93ab6d00f214e MD5sum: fc0158978044dff58602df3cd2d61efa 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: 0.28~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.28~nd11.10+1_all.deb Size: 5734 SHA256: f733456d43ea7401848e11689ddec2e155cfc942bf20b71e4f1da7aa77ec6059 SHA1: d21dbe88eb80206458887c0b5d79f2f537289b62 MD5sum: a3ab42af83efa5916f05616a8cc02ebe 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: 0.4.3-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 652 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.3-2~nd11.10+1_all.deb Size: 614512 SHA256: fa9c8d3217a2a976a5c9c0fe80d74912373d34cdd5c83e257043e1e2b0a894e2 SHA1: 669460c9656ae30f6a8e3f0657accce7800110ab MD5sum: 79a21b65d7f3d316cc23868fe5e9069c Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nuitka Version: 0.3.23.1+ds-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1696 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_0.3.23.1+ds-1~nd11.10+1_all.deb Size: 331758 SHA256: e9ddf794984904e2a979e980ad1886283de7011da78ecd542861e429c86b3246 SHA1: e8ebaa5e98f14d9942d9f99df04d57f72042e45e MD5sum: 742dc04f41b89f42da5c0b85842cb720 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: packaging-tutorial Version: 0.5~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1155 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.5~nd+1_all.deb Size: 1111034 SHA256: 3410f99232ee6a8cff11c2d97b4cd50f56d4ae5d71f5dadaa077d92457842996 SHA1: 8756d44b1a608c8c0e29fde5813d6146e67c5026 MD5sum: 7d653f7b7bc96d627e73720627567851 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: 1.73.06.dfsg-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5276 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_1.73.06.dfsg-1~nd11.10+1_all.deb Size: 2688766 SHA256: 32a9e0768cf5bf9591a1075ec7dc90c208f21e4922a74e3965bb8baba00b6bcf SHA1: 0beea31880cb2fe2269341b3a6fa3e1b41432a09 MD5sum: a0ef8b477018f3a2b8882630beccff2f 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: 2.6, 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2579.dfsg1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 53608 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~nd11.10+1_all.deb Size: 19433958 SHA256: 2f4a5f3494d946e1c6b76c65d0624455b48779e6db75a24ec49870b73eeed8dc SHA1: 5025d36253ebcfc432534bfc49fde48d6a2e1dae MD5sum: d9af8d816aa59f69432c8dea308bc4bf 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: 0.6.4-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 252 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_0.6.4-1~nd11.10+1_all.deb Size: 51606 SHA256: 489c846a4f5c9b6a1f3dc32630efca30394c50084d836cec1bfb789826be2f21 SHA1: 7db0e60648bebb46f3d950643730b9a16904d4dc MD5sum: 479a64cce8f3c8a95b8dd79bc80bacfe 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, python2.7 | python2.6, 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_all.deb Size: 7320 SHA256: a6173047b4834e63ae2a25dd4d1df3d705b14a56a878e9e6ada4828e22938a38 SHA1: 8727ceef57f94c0c47c1394ac652d0860eebdfb5 MD5sum: f45dad983f038670b80cf5904d49f7f6 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1908 Depends: neurodebian-popularity-contest, python2.7 | python2.6, 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_all.deb Size: 482024 SHA256: 0090e5b0b1fa5249740e5aed500b557699fff4783f9076a3da180bfe64e548e9 SHA1: 28a63b4661ce34bd8a3c22fe98292d5f05f0f8d1 MD5sum: 5150750ae8df07edae697f7fada799a5 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4104 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd11.10+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: python2.6-mvpa, 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_all.deb Size: 2205048 SHA256: 80c6f60748519b38a980781555bb3aa9e4aacb37b559e181395cc0cc592fb2ff SHA1: 3c565e370c198a6c4ec10822d4bc45eee914ba0a MD5sum: 79b54669d7f087b1fb11b010cb33d349 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: 2.6, 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40796 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_all.deb Size: 8486982 SHA256: 1c6b0fedbfa215a04e43cf8cee1152348a62b66c69effb13ee846261db73adf7 SHA1: 0889baeb9497ecccc2a534d93e8bf9520051d14f MD5sum: 22620f53bb3d0bd9a208aaaefc240687 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: 2.1.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4800 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), python-support (>= 0.90.0), python-mvpa2-lib (>= 2.1.0-1~nd11.10+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: python2.6-mvpa2, python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.1.0-1~nd11.10+1_all.deb Size: 2354504 SHA256: 6b5eb44613e1e781e545081db86ed160483b3a9dfe275fe97191ccfebfe969c1 SHA1: edf6e182e482da883599fda3b911f02f03690b25 MD5sum: 4cafea4e86627d01058a1f045d8e0f37 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: 2.6, 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.1.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25196 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_2.1.0-1~nd11.10+1_all.deb Size: 4910632 SHA256: 743b5852dab246a86a90ddd1d7d26a4d753159bc62665bf4e9f5aa771d5b9b04 SHA1: 8bfcbed4ed0302125b63d6f992c17b3d9da67f1c MD5sum: 41fb8dea857cb7a06a020b328345f57d 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2488 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8), python-numpy (>= 1:1.5.1), python-numpy (<< 1:1.6), 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_all.deb Size: 1372496 SHA256: dd770113fa29bfec8539a9e339c67da88648bc43df871280463c806b77656d20 SHA1: 4c280b8508a747ee7ee33ecefa08fd813149bd4c MD5sum: 7422ba6273701c7e00a492cbb88eae40 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: 1.2.2-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4448 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.6-nibabel, python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.2.2-1~nd11.10+1_all.deb Size: 1812918 SHA256: 3cfc22131353c759113889403d6df37e029b94a9a8f7da1c2e910c3ceaa22ffc SHA1: 26caf9b6dc8b670cda3d3b43d0573f9912e3c122 MD5sum: 1c6caeffc0d1c02b7f138d55ff9bcb65 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: 2.6, 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.2.2-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2852 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.2.2-1~nd11.10+1_all.deb Size: 418138 SHA256: aa40b2be09802db304b3b8046b5cd96c859fc3e04f833c834bd0ffe8f35ee8ee SHA1: 652fd5fb5de7da63ee7fe2d42f615ebee3aff6a6 MD5sum: 63d8250caeacd6485ff94b7338ea20e9 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-nipy Source: nipy Version: 0.2.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3764 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.2.0-1~nd11.10+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.6-nipy, python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.2.0-1~nd11.10+1_all.deb Size: 763128 SHA256: b04eea5a4e729927d7f9a934e1b4446a086e5e1c56fee8e81cecff392e8ad817 SHA1: e9e48f1a4a34e0d93bce0369fe1fe0b04251db12 MD5sum: fdb9f9a1917c81a01c5c8e95caebe1c4 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.6, 2.7 Package: python-nipy-doc Source: nipy Version: 0.2.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9692 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.2.0-1~nd11.10+1_all.deb Size: 2495506 SHA256: 93a0ecd6bf0936a6f611c459040b4dbba1df8bc300860d3520224700549f7556 SHA1: fe1e2c0a2ca54d2c1aa12171165e755df8807948 MD5sum: 913dd9beafed84a2881334226f353359 Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.6.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3064 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: python2.6-nipype, python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.6.0-1~nd11.10+1_all.deb Size: 521752 SHA256: 76e186abafa0ab8111d1bf5e1685a0ec73228aecdee9fc6396b89c754ab4deec SHA1: 7b6ff9f79cd19b121ae9fe4246b026166d2261de MD5sum: 2cf15b66850ff9123b06f4f1443ca3d6 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: 0.6.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13488 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_0.6.0-1~nd11.10+1_all.deb Size: 5797018 SHA256: 603e8c7620310334e4c37cd9d81ebf934d0db45ed3af43414c6f8c20c98e6ed9 SHA1: 8c507d8a9630441a40cc80e39cb131efb72cf218 MD5sum: 0451e7935b7b01d9c1379a43babf8b19 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-nitime Source: nitime Version: 0.4-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9444 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.4-2~nd11.10+1_all.deb Size: 3908920 SHA256: eb82dbfb873469b52d7d46fa57b1a8e806b4580f95847d2156b77ac9fa451a92 SHA1: cf437a6279966daf3ff7ab27957cbccad7378a40 MD5sum: dd1a12936e95c4c6f7b7ee462db5ef5f Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.4-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7104 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.4-2~nd11.10+1_all.deb Size: 5271362 SHA256: edd183edaea4d04fb9c8312fd11bf16c6a6aa946d746a020bf53ddbcac1d0429 SHA1: 676be025c8a5320c90b99359fabcab2e3b023e3d MD5sum: 92f78c40fee0405ddefe5ae6dabac0c7 Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1612 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt, python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd11.10+1_all.deb Size: 245078 SHA256: dbd0618beef263545059368cce47ecc8ca5219cf428081e9e239c61a7b4da37f SHA1: 78ed3d3d491d1025833ecc5c1f891da94783c243 MD5sum: 220377b2ffc32c02689afb73de092625 Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6, 2.7 Package: python-openpyxl Source: openpyxl Version: 1.5.8-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 504 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_all.deb Size: 71610 SHA256: e271b66c069fb3b410a14efa14ccff0498184fc25d57b2e69e7ab259b5f9e66c SHA1: d74fdc8774860beec272f7b5e1861a7b6869edee MD5sum: dd19329dbc63054ab3424eb06fe85d43 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2220 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) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.6-pandas, 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_all.deb Size: 460900 SHA256: 1f007666eb9baeb1ce834bf32c88dbea86de368ff864c1b1db2e0426d697e9f0 SHA1: 37366ddb925a3eb1abd60334eb4e6bc96583b1f8 MD5sum: 0be0bce12ac8e0683de58b3b0950ccf3 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-pyentropy Source: pyentropy Version: 0.4.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy, python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd11.10+1_all.deb Size: 21332 SHA256: e0d255f8e67cbb1157e49c1486b4f1130df8560959a0ae7f72f844f8ead02fd2 SHA1: 253d2f3413a555abad753170ee53f53fea933416 MD5sum: 6f065ec917691608e170ab909493600b Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.6, 2.7 Package: python-pynn Source: pynn Version: 0.7.4-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1008 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.4-1~nd11.10+1_all.deb Size: 175608 SHA256: 516ceca23e1540f8832d08806818f302a0684390dd2a0f9b7b3e926d73fc9fed SHA1: 916ae694e47bde872c88ea9a5748b6ce27cc1dc1 MD5sum: 74ccb92ec56d6b1c3b2438569e1be106 Description: simulator-independent specification of neuronal network models PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-quantities Version: 0.10.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 504 Depends: neurodebian-popularity-contest, python2.7 | python2.6, 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_all.deb Size: 60204 SHA256: db3834ba60ab0a28385895e01e87ee77196d468252a2196c4396322aba9b8032 SHA1: 3debe5c10b56978500cc395f0a1ea825f08ab3ff MD5sum: 31cd2ec5a63a5d73d902c83d66008e7c 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-learn Source: scikit-learn Version: 0.11.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, python-sklearn, python (>= 2.6), python-support (>= 0.90.0) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.11.0-1~nd11.10+1_all.deb Size: 22864 SHA256: 461a735674d7c63c5a26e4d176cb3f6f5fa7ca38740e9963928ed7f18572ba54 SHA1: 7c5887184b0c803c9a0ff5a605061596d663f653 MD5sum: 9f717f5a9bdb76b360583015fa7e13cf Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.4.0-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 116 Depends: neurodebian-popularity-contest, python-statsmodels, python (>= 2.5), python-support (>= 0.90.0) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.4.0-2~nd11.10+1_all.deb Size: 7176 SHA256: 53d5ae442110a0cc00eb3ac0e84c1af9e15cad473ab399410799ea048246348f SHA1: 87e91cb60dab714fb4072e44bda3e188b84b197a MD5sum: b41b5cbecc293433aa4c631ff12fd462 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-scikits.statsmodels-doc Source: statsmodels Version: 0.3.1-4~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20736 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_all.deb Size: 2666076 SHA256: 70084b0225c7c438ff33eef9c5229f338b28878caa444f5778b82b8427f10967 SHA1: cacf440471a8de730029ce8ee8cddd04b6e6279a MD5sum: 49e51765e58d515de79e107055338e8b Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: python-skimage Source: skimage Version: 0.5.0+git100-gfeb3e92-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4060 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python2.6, python-scipy (>= 0.9), python-skimage-lib (>= 0.5.0+git100-gfeb3e92-1~nd11.10+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging Suggests: python-skimage-doc, python-opencv Provides: python2.6-skimage, python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.5.0+git100-gfeb3e92-1~nd11.10+1_all.deb Size: 2526508 SHA256: cbcaa961ba15535bce93d0976654457ecf886641541d1b930cdf8d90d1f8638d SHA1: 41280a0b4a1d5a476257885f4e87d657d019efd1 MD5sum: e06604621eb16416e7aad48d3b91b832 Description: Python modules for image processing scikits-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. Package: python-skimage-doc Source: skimage Version: 0.5.0+git100-gfeb3e92-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5148 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-skimage Homepage: http://scikits-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.5.0+git100-gfeb3e92-1~nd11.10+1_all.deb Size: 3652802 SHA256: e68f8ee76d235413e761fa8b6b2562e8cd216b5f5d6c055d7e0acc5c0059c81f SHA1: d665de630b1640eeb3ae371e8430e4a4e19cda01 MD5sum: 288568578622862041ac2a7dba438d33 Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.11.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3072 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.11.0-1~nd11.10+1) Recommends: python-nose, python-psyco, 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: python2.6-sklearn, python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.11.0-1~nd11.10+1_all.deb Size: 890932 SHA256: f3326d151a32ad1291af89d83bbbc262cd6f0905ce499609ab79eb5f5039467f SHA1: 93e44c200cd30d95a9e3d372c7acadf067da3170 MD5sum: 3aa5619d32957cdef2941d641aa423ea 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: 2.6, 2.7 Package: python-sklearn-doc Source: scikit-learn Version: 0.11.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30620 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_0.11.0-1~nd11.10+1_all.deb Size: 17600004 SHA256: af80f50b94a17fc0d5bc1066a83b97379ec34ce071da1525750ee4683563915a SHA1: e32649d7bb1d7ba6a200479d4724371e7c022aa1 MD5sum: 8d548df8cc1d55946895ee21a1e3ed0d Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-statsmodels Source: statsmodels Version: 0.4.0-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13488 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.0-2~nd11.10+1) Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.6-statsmodels, python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.4.0-2~nd11.10+1_all.deb Size: 3079214 SHA256: 489e3c6232aafa2cfda5ae0911c3259e99b04eda30306fa5a09fc4f7aaf06b13 SHA1: 0675cf34ab10e32eac2bc54074c3ae79ac60d068 MD5sum: b02001cd8541fbd71e3643c1d7fc7c71 Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.4.0-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24300 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.4.0-2~nd11.10+1_all.deb Size: 3964892 SHA256: 48f3e09c3c0661bedb56510eff105a31091c97c5b08497817c762a5be51db0a8 SHA1: cb9838c35006ff747146fcbd72e6d8cfafef7ce1 MD5sum: de0fb28f7e4b3c74246ae1dc345531c1 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 152 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~nd11.10+1_all.deb Size: 28006 SHA256: 2b8658acba6ed0dba471f47cb6ca6ca3dc81757daf42c1b72924d263ccfc605f SHA1: 35ae235b413674a69a5baadad6e09753cfa1ab7a MD5sum: f3c063ce47419d636db1c3caa446e656 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: 2.6, 2.7 Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22352 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_all.deb Size: 10573720 SHA256: f0321ca7b21561e4d36761c8b822a658935beed84da70603e6c85c49c8df54a7 SHA1: a90f50f9eaf6dc3c4dafb24896e014b1f46ad17e MD5sum: 60163a673bd38fc493e44f6ff60f68e9 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73084 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd11.10+1_all.deb Size: 52167722 SHA256: e19c67624dae801e90d3d2ac4513fd291a52c679dda6a17539f9b81281400e8b SHA1: a4516b85aed0b0507ffb9fb655f77fee607896b2 MD5sum: 6f8b8c766b3a7a13083842d70d61f325 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 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9380 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd11.10+1_all.deb Size: 8648920 SHA256: de82f4ec4f61b92d16f055186e425295077205616998b56a507c773648c00eb2 SHA1: 4f226546eddd4adcc049cf993c2dd1bebefb696c MD5sum: f78a3cda6f161c0975499c70718050ee 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.