Package: condor-doc Source: condor Version: 7.8.2~dfsg.1-2~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_7.8.2~dfsg.1-2~nd11.10+1_all.deb Size: 1328028 SHA256: 7dca5dcde52d25b994e33f6dc73c59495481aea773ae9107038db53673a53186 SHA1: 7812a359b9134fc31a9ded7b97bab919b3b59687 MD5sum: b338d53d76eb2345f5d244dca761c44a 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: freeipmi Version: 1.1.5-3~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd11.10+1_all.deb Size: 934 SHA256: 9a74a802658e54c5939bbd5ebc21b4fc7d70900d0ad520046109457aae980399 SHA1: 940b77fe6cb6191a17460422dd771cb7d2af0ada MD5sum: f28f330b4b4051ab5a1944ba813890a3 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This meta-package depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.1.5-3~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 472 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd11.10+1_all.deb Size: 296938 SHA256: 887bb67cfa7d82ac20dfa9d4a4e6f377aba44a39bd05632950f7b9a8f4ac4f85 SHA1: 2bb34ca7dbd18d927c228b6a482aab1db19c330a MD5sum: eecf6cdbc8bf6f9e07b545953301009e Description: GNU implementation of the IPMI protocol - common files FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package provides configuration used by the rest of FreeIPMI framework and generic documentation to orient the user. Package: fslview-doc Source: fslview Version: 4.0.0~beta1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3112 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.0~beta1-1~nd11.10+1_all.deb Size: 2346148 SHA256: 634221beca94dab94eb32ded9eec501f7346946ffc4935c07cf7564c8b67c105 SHA1: edd1f9f470d287557f085ea688dca3a552b5294a MD5sum: b2deb8c4d7e21dd4745954b4326e4dd2 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. 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: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd11.10+1_all.deb Size: 9670 SHA256: 38fd834ee0212bbda9b6e23163c1f144f36bd94db407ac4f88e841b39f900e99 SHA1: cea79dee3c33ef42b2d65ec64e1cab7f2dc10db3 MD5sum: 7ca3af87fced0a8134eb92caf3e0281b Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: ipython01x Version: 0.13.1~git33-gcfc5692-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6200 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-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.1~git33-gcfc5692-2~nd11.10+1_all.deb Size: 1304514 SHA256: ee831ea09ebaf0f6e3d3da4ac5665922a78dffe88296bb206a91c0df0bcc4653 SHA1: d6919491b3a54969353521483033a9145107e228 MD5sum: 39e0187e2182f2e6fa2faa9425c8f38d Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.1~git33-gcfc5692-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17728 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.1~git33-gcfc5692-2~nd11.10+1_all.deb Size: 7024750 SHA256: 206fa65cd7c9c6f101c4cadd4163fc8263a5160a4debed4cbb17948a16576662 SHA1: 0d4be84d8dfe30eeb9ccfb1c6d9e84de755a9869 MD5sum: b8630e2fc206a4029b17726714b82728 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.1~git33-gcfc5692-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.1~git33-gcfc5692-2~nd11.10+1_all.deb Size: 896 SHA256: cb69fc1ef37ff37d8adf2fb0cfd0b18d13d742c24c7303b74b4886d42d5851be SHA1: 25f1c654bfa72c502eb143fa0e2d27b36d1b5f4b MD5sum: ee68a17c9190f7772f06e6cbe490a885 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.1~git33-gcfc5692-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.1~git33-gcfc5692-2~nd11.10+1_all.deb Size: 824 SHA256: f16b920cb5cfdcf0e8cff8a0258229d2dd94979005328e61c5d28a8302a220d8 SHA1: 0d44daff28eabce999d8a9b701577c07758a04a0 MD5sum: 0f5e2620ce2b6251fb43798d1a593e6f Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.1~git33-gcfc5692-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.1~git33-gcfc5692-2~nd11.10+1_all.deb Size: 910 SHA256: 65c73ac977338179bc03dc6569539454d5f07ccd96fd2fba8b9afb9806b1a998 SHA1: 35481636831c56b1dbed8bc8797dd71e1613102e MD5sum: e2e8a89ad2a37a6a0f36e0c1c73da99c Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: 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: 0.29~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_0.29~nd11.10+1_all.deb Size: 114502 SHA256: 0fd49c325048fac359b6fc08325382598109823c76c8e62ab1c58f6aa6cf5ea6 SHA1: 66893bb9b364494ed29594293f59ac4c268e3189 MD5sum: 3cfc7d6f1b94257a5ae1a1afbc73fb0e Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.29~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_0.29~nd11.10+1_all.deb Size: 5346676 SHA256: 485d2c5db472241ccb45dfc572d820ba5e95a3ecc17caa3c538b91317b08e2e2 SHA1: 81d9b5d9d13e60daec08ea719ebe0e95aa71f82f MD5sum: 663cc302bac6420bd8e04119da16181b Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.29~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_0.29~nd11.10+1_all.deb Size: 14242 SHA256: 3e5b4409877c2d8534bcfa3c645a51a6ab7c3b5d2987d8927575f45814214b8f SHA1: f6a2fef54e661aa9a5c116500d0469e5ec29deae MD5sum: 5e34f4a53572f8787a847b2d0f013bd2 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.29~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_0.29~nd11.10+1_all.deb Size: 6930 SHA256: 5abcce47c04b1e4edae85c4a879624cd5c81a2ed8602f16eb1949488e9e31db5 SHA1: df854b77ed5c8bfd3d49cfee05cd893831a33ae4 MD5sum: bed8cec79541634b1d9d248132717eec Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.29~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_0.29~nd11.10+1_all.deb Size: 6096 SHA256: cd3d6baa32576ae2515c114dd5defe00b7e1541e4573eec489007c1807d54373 SHA1: 02b5dcd50debbc1c4046e3f0ced17e85216319a7 MD5sum: e2277c5bb020db8898646de9dd84584d Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.5-1~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_0.4.5-1~nd11.10+1_all.deb Size: 614936 SHA256: 7da4145c0fc639d49a663f8fd245dc7828f551cb9d8b5804638f8592e8bb9b03 SHA1: 29f0ca3e498741fed0b3d038fecafb740482aef0 MD5sum: f5fa8946719c3d9c62321a8df017a720 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: 0.3.24+ds-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1716 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.7.1-0ubuntu2) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6) Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.3.24+ds-1~nd11.10+1_all.deb Size: 343428 SHA256: 738bc3304f3c98696f4c9bd3b80727a8727e345e305f6168b4c76ba619bdbcc8 SHA1: 9e57f4f9c95f3050e85fdcedc4c9447c678ef9f1 MD5sum: 0e9507ff5c0510ceeca3f252309ef4d9 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: 0.7~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1545 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.7~nd+1_all.deb Size: 1482008 SHA256: adc5cfa1161cb2c81de6dfe8ef28337496f4482dbd4e81529fdca5bb7f99d234 SHA1: 9326aef75840496b2113097a67ab254223056afe MD5sum: 08e90e8b604b39dea04f6eb7b4359b21 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: 1.74.03.dfsg-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6040 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, libavbin0, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.74.03.dfsg-1~nd11.10+1_all.deb Size: 3102236 SHA256: 4c1c7f63a8c63f34e3d4f9e747376c1194765c4beb2ec1de5bffba1db1df8d1a SHA1: 58b0e66efb9d21cc619037a7ea0e47be3d79a97d MD5sum: f52fcfa7b15f829dab048afa0bacb300 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-brian Source: brian Version: 1.4.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2728 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.0-1~nd11.10+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.0-1~nd11.10+1_all.deb Size: 503170 SHA256: 8267ca17fe4bb29ab445339b34f2a487033f46c7937a2262f3e3994c1785f75c SHA1: 5b818c6f4398e0d62b6141fc19b63cc60456c2ec MD5sum: 1344075bd07a435e70531a69c4517333 Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.4.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7216 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.0-1~nd11.10+1_all.deb Size: 2158368 SHA256: e213f7e2f23066fd141fb37f006793472b2391007900aec6259aad83ea02dbc2 SHA1: 1b306410c6d6b2524516156a99e35184d977041b MD5sum: 24860d6a9a5d2a1bb9d503e087292e83 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-dicom Source: pydicom Version: 0.9.7-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2032 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.7-1~nd11.10+1_all.deb Size: 425406 SHA256: 1431b078744d8cf21fce7a9f73e09ab42e840a375b878811665d78af83cdd681 SHA1: b47da33e7d6efe0e27d3d05184e7a27a5355c74f MD5sum: 5367f033ea76911e18cd97b673848176 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-joblib Source: joblib Version: 0.6.5-1~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_0.6.5-1~nd11.10+1_all.deb Size: 52646 SHA256: 86e3a50c6b8f1699a8e6e1654025f698b62419c9c2532868956dc24e4b30d309 SHA1: 9dfdc5e34493293947d54f40d7fe59aa945f4475 MD5sum: 54d9e9cbce92fef3de8d62aa6e667a4c 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: 3.3-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1912 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_3.3-1~nd11.10+1_all.deb Size: 483674 SHA256: 0c284a67ec8059c67575a4c09460cb027be14d6e36caf9a037891d497f38cf6f SHA1: 598524ec7fbd87126dcae7e7b951375385b9a833 MD5sum: 44c6e2229fb087f68588d0d0fa32db4d 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: 2.2.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4956 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 (>= 2.2.0-1~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_2.2.0-1~nd11.10+1_all.deb Size: 2399886 SHA256: bd0bc91ca1db605c946c375daa380d7b3fe850f8a514f7b5d86672360cd5242f SHA1: 6bca5a841fbb11b7c7fa10bb586933ce73e60d45 MD5sum: c4c35132ce09230d3c0c57f8f4cd051b 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: 2.2.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26792 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.2.0-1~nd11.10+1_all.deb Size: 5177596 SHA256: b55454cf9f6883dd963952c334218f32d21e26865b45bf7f2863f6841ab349b2 SHA1: 8007c27b466c60519e39620ed1e6f47458c869bf MD5sum: c1c33c87f29ad2eed170f3e4ce3225a0 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neo Source: neo Version: 0.2.0-2~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_0.2.0-2~nd11.10+1_all.deb Size: 1372760 SHA256: 29d53f881d5c2cfebd97379fc75ea8dabf14c3dd015ead1aeba4e366f3939a78 SHA1: 09fb69fa97a26d7592eceb0560e1296d57a4e989 MD5sum: 693ecd3d8fcfc6b59178e69a5c988a4e Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4468 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_1.3.0-1~nd11.10+1_all.deb Size: 1816568 SHA256: 31703c73d3a7529c9661d58a06525b245eb23cc89237879e795f22da3d50cd65 SHA1: ee28d5b9538d4552b4750b305fb6d1df82d22b9d MD5sum: 4f8eeea16e17cd29eb2c6fe4183bc7f7 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: 1.3.0-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2856 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd11.10+1_all.deb Size: 418580 SHA256: 81aa0e70e635ef4a48b15ed7173552e6adf3e72209caf5fdd87b13a8a9b8ae87 SHA1: 7b93d5f20f371ae26cff4fe9aa9d9c83ea447e2a MD5sum: 0a9e5c7875e7554a5d4eec37b28b7755 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: 0.12.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.12.1-1~nd11.10+1_all.deb Size: 24320 SHA256: d45ac74415d433937f8c28baefa296919b49f17be9aa3127c66985003a8e13d5 SHA1: 70607060661b79e212d60bac7ffa3abf7e60b835 MD5sum: 1c4646d3dae1e37a7aea000223571759 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: 0.12.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3204 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.12.1-1~nd11.10+1) Recommends: python-nose, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.6-sklearn, python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.12.1-1~nd11.10+1_all.deb Size: 927454 SHA256: 99bfcef512a93f8117b09be8106afef5feff491a6c3b3e947f1cf620bb5fa866 SHA1: 1a4ee717f68e79d44811757e23c9676415a89b00 MD5sum: 41f9b6fa437c7346a0782a9958810e56 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: 0.12.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31868 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.12.1-1~nd11.10+1_all.deb Size: 18384372 SHA256: 6f630c0e90c74d8317c93fa41e4fd59a972197299e5962114d11eddaf1e0010f SHA1: 29e9c5309fb27ff2e32df299b8ef0fe3cab23927 MD5sum: c0bbc66eaf2cdd5e085e0426b21ed606 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.