Package: condor-doc Source: condor Version: 7.8.7~dfsg.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6964 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.7~dfsg.1-1~nd11.10+1_all.deb Size: 1333684 SHA256: 9e3e08eba374f37490e9e93cc63a0409026fe882bb5f463b5fd409396b0dee47 SHA1: 4643ddd08a3b01dd2543a108d7c0b200a9a806ce MD5sum: 5bb946af2bc61808b5e0d0f1554fe17d 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: 0.8.8-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 644 Depends: neurodebian-popularity-contest, python (>= 2.7.1-0ubuntu2), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.8-1~nd11.10+1_all.deb Size: 112572 SHA256: fa2ccb908a569700836464ece625c96add8ccc9b09021d8d78a21e4a3dec7e7f SHA1: cd132a46291674d47f68c881454c7e9ee6091fdb MD5sum: 8fb10571dbb4c723f4ade2c45b378851 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Package: 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: 4.0.1-1~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_4.0.1-1~nd11.10+1_all.deb Size: 2346430 SHA256: 782dc4e7a2fc6e5c29ee0f0e60cb4724f75c853aa3a275e0787708965757a3db SHA1: 797337dea787ab8aceb9cd0ab086553ca4af5e8b MD5sum: d30eefda89d83c78b5b5d17fbd4045e8 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: 0.13.1-1~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_0.13.1-1~nd11.10+1_all.deb Size: 1305208 SHA256: 37625646a9f8ddbd2ffa11bac0642a8dc7c163290addce4d57cdd2c352aed5c6 SHA1: 76c2d556390337d95a318d56df15ec71cde183cd MD5sum: 7d7ae96e17b678021caa9fa82e65fe29 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17760 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.1-1~nd11.10+1_all.deb Size: 7035144 SHA256: 4b9b8a1aa8103a846a94f37230752119a6a8b8e1ab7f611ce9adef826d276c10 SHA1: afcc086c1adda9eaac83aa1a9306c06193d248eb MD5sum: 540ee6d98ba40b258d18803c76e2623c Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.1-1~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_0.13.1-1~nd11.10+1_all.deb Size: 902 SHA256: d5ef218e9ec0af5bef2147d08183d898f58794d589bfbc366841a9bba7b90969 SHA1: 3f5b98b4bfc96cf3c0e326ee897dd35557cd45aa MD5sum: 11f71651a9ef5b65a5bbedec175890b9 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.1-1~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_0.13.1-1~nd11.10+1_all.deb Size: 828 SHA256: 9f6ff5654e9314a245ccfdaa3605f453b688d358bbe7ac0c527cb3551dfce9b1 SHA1: da261d1e6b19c52fcd6f584786e56eb0edddd4e9 MD5sum: bf5664b8a0e51c1010d3d742dcbf9524 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.1-1~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_0.13.1-1~nd11.10+1_all.deb Size: 912 SHA256: a26982caca8b69930730d43ec1483dcdb9ccc06a5387e47fa7112e52c8c45db1 SHA1: 5ac770f359a38684191a83e8681703c64def31dd MD5sum: 4c80cd72d4ef94dd2e03f262f2891ff7 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: libopenwalnut1-doc Source: openwalnut Version: 1.3.1+hg5849-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36500 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.3.1+hg5849-1~nd11.10+1_all.deb Size: 3904888 SHA256: 20bce21f498dd895e06b333129a4979e8b68348ce3046d38548d536534289949 SHA1: 148a47abc7cc80e9f87da3345a538e92caa8a0d1 MD5sum: c43597be89b069d69d2e02a6c80e1cf4 Description: Developer documentation for the OpenWalnut visualization framework OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: matlab-support-dev Source: matlab-support Version: 0.0.19~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.19~nd11.10+1_all.deb Size: 7222 SHA256: 9c8a8decac151557548479bd7e1c9c23267d92aeacefcc89a4d2a83750d0565f SHA1: 9796919d5a9567ab984656934e3485f60b697caf MD5sum: 924358bb64d2a143d55abb0c823cb8d2 Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. 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: 0.30~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 272 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_0.30~nd11.10+1_all.deb Size: 115078 SHA256: 97fe320cf87015955f2df3a9acdb9de0d75fad3634ceacd760467f7dbd2fc932 SHA1: 42584db8326797bbee456de6b1c9b5d9d4448a9a MD5sum: 1c278d71fb4301b873d605a2d57e00dc 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: 0.30~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, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.30~nd11.10+1_all.deb Size: 5348092 SHA256: 8440d68948a362c18333cb7dfd76d3ba60df582f044898a9455cd558786abecc SHA1: 8340ab9d3d52dfca8ac67b63193a8e818193718d MD5sum: d4ca2058e7f2f12ff4d09e51f1fb298c 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: 0.30~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 | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.30~nd11.10+1_all.deb Size: 14940 SHA256: 7aad348102f7975dd0a35f084ec650f877790330230989f6a4da21945dff63c1 SHA1: 44b03fdaf9636003467b10cfa875d43d8eb47c40 MD5sum: e1d18b34ecdf80222641e2e99b3c6ce9 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: 0.30~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_0.30~nd11.10+1_all.deb Size: 7270 SHA256: 46936e7ce577df02f1b5421363d0d25c171fc4f07cb949bb65e1415f4aafd49b SHA1: a71b6d311b00812c0c0dc1aec633874d426d705b MD5sum: 53dcc82ca2e7be4cd413267f6e42419a 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: 0.30~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_0.30~nd11.10+1_all.deb Size: 6430 SHA256: 517cf3401dbbc1ec2791c6720d82db03c03e9b71e27741031953de925b65066c SHA1: f069c21a006466ae460c6afc65891fc3a9612879 MD5sum: 783c219ee2d002701da438b8bf2a0ce9 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: 0.3.25+ds-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1748 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.7.1-0ubuntu2) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6) Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.3.25+ds-1~nd11.10+1_all.deb Size: 349472 SHA256: 6a7ea242b78b62a7ee3b9f68742a93b463675a4b102b9ce44b2fcae77ee0ee31 SHA1: 1da698890877111b0f293eae1a98e5dc6c589a20 MD5sum: 9bd60b3a8e0b69acdbcf0d3f5b67e1c6 Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class to pure Python objects at all. Package: opensesame Version: 0.27.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26168 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.1-1~nd11.10+1_all.deb Size: 24042622 SHA256: 8ffb555f154919db1a73348643670368a5b34b83084ebe45717f3e0366de2016 SHA1: ae480a8fee418f87a5a3b4bae9ce2b42de320d6e MD5sum: 61e09c058cc0cf30d8e19d96fb283756 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Package: openvibe-data Source: openvibe Version: 0.14.3+dfsg2-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9860 Depends: neurodebian-popularity-contest Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-data_0.14.3+dfsg2-1~nd11.10+1_all.deb Size: 2024480 SHA256: f5dcf6a8305554b34071c5c91226794d926a710640271a5931a4178462a1c004 SHA1: ded3fb81f200739236836663ebf5beb074f7db2e MD5sum: 05989c0100e43807b223fb490f46ce52 Description: Software platform for BCI (Data files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the data files. 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: 1.76.00.dfsg-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6252 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, python-pyo, libavbin0, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.76.00.dfsg-1~nd11.10+1_all.deb Size: 3177692 SHA256: 332a1e667cda9c179f3aafc68b24af3f6e250af326e1a2f228af5dd27158494e SHA1: 50a1a2f25d4a64dd9274f707a4a7463642701211 MD5sum: 6a8072d63c18f1187fea55d437b48ad7 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: 3.0.10.20130114.dfsg1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 56604 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.10.20130114.dfsg1-1~nd11.10+1_all.deb Size: 19678536 SHA256: 2086743322f6dc3967295d38be59e03ecfe634e1c6a8d0c3bdfae4fddb583b88 SHA1: baf9ad98224b8f666c710e5c6a055698e990650a MD5sum: 938667d4d10313e478d7cca8d7939f67 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+git6-g7bbd889-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1916 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+git6-g7bbd889-1~nd11.10+1_all.deb Size: 484220 SHA256: 5f9662803c96ac327d480ac52ba2a91337194666d291134e9d8c760a6879375c SHA1: cdacfe30cefade1651537d62bc23f0f0574eeee7 MD5sum: d0e0d6dbf5a1838805da508d1b55ba08 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: 0.7-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3352 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_0.7-1~nd11.10+1_all.deb Size: 567700 SHA256: c46719893775c3cf77488604e3695f8deb2c60620f99a9c5ab784da62368c5b5 SHA1: f1e470ff650d9c8c636fa2b59a2f855de1951fe0 MD5sum: f452aaf5ea974b39f2242f3f988e807a 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: 0.7-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15172 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_0.7-1~nd11.10+1_all.deb Size: 6694962 SHA256: 8d1503627da7aea96429e6d548bfb76af68c143596d8e16002ac739f76eee527 SHA1: 10adb0392705c3931a6cc89d2f0c2064db828f24 MD5sum: 57850b0cc7433bd90673480a29c2a31e 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: 1.6.1+hg2-g4bff8e3-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 404 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_1.6.1+hg2-g4bff8e3-1~nd11.10+1_all.deb Size: 62046 SHA256: 2ed93a9990e1a6d79b01d1590b02913b892502a34a1bcb7e2e9ee05e1e679ff2 SHA1: c0df2c2659777e8a7186e62f8aeac3c0eb6f291f MD5sum: 611cb634dd61a28f8b84191d39df77d2 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-pp Source: parallelpython Version: 1.6.2-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 176 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd11.10+1_all.deb Size: 34280 SHA256: cf10c101240f57f6490dcfe517641ba4c4cdf1be8e848a82cf0bc8a96eb15022 SHA1: 0269d308912dd34a97527c29c1624ea50e8727dc MD5sum: db335dbdd2fafdc1666c09bd415002b9 Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.6, 2.7 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: 0.7.5-1~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_0.7.5-1~nd11.10+1_all.deb Size: 175774 SHA256: 92d0dccfd9f848ad55ea3e05d2a75a1b640d34864d499c71be15c2d405d3ee3c SHA1: 18fdbee3bac1bfb57a519a118b4b282b2a7ead24 MD5sum: cee1cc7543c34cf2eb42ead51d0945a9 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 672 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Provides: python2.6-pyxnat, python2.7-pyxnat Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd11.10+1_all.deb Size: 107400 SHA256: 74c780f87f767ec3bba44385db4e9feb09de073e8c161621641968dd4b92671b SHA1: 9fb7d09c9afd8d4182c488db8b80971d29c4a7f9 MD5sum: 7ba228b3bdbce2027ba52e86a1f5c1ff Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-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: 0.13-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 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_0.13-2~nd11.10+1_all.deb Size: 28142 SHA256: 5dc6e3f4a799a14a9e294fc73312f6834980c44b0254d5ed04f4ec8bd97cb416 SHA1: 4a0a75fcd1d83bd8f1f0ab026a03358d89a1fbfb MD5sum: 336091d199b3536b6a854b893462c767 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: 0.13-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3600 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.13-2~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_0.13-2~nd11.10+1_all.deb Size: 1007460 SHA256: 7fcff52818972f2789700a4c83aedf9110283f106027e82053e8ea73987c9478 SHA1: c9eb2da15df07b21dc7ed9173c2845566faabb55 MD5sum: 08f782c5cae3f8e8af041cda4f653eff 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: 0.13-2~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36564 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_0.13-2~nd11.10+1_all.deb Size: 21151770 SHA256: 3b0bd9dc9372b2fa1098aaa504a62696d20921efeee14fb43fcb06dd49b84b9e SHA1: 336823aa96b78bda28b93df11f5165a27d3fe75e MD5sum: 5a6c7d627d4b5a1a2f21b3bfe98c5af1 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: python-tz Version: 2012c-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 160 Depends: neurodebian-popularity-contest, tzdata, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd11.10+1_all.deb Size: 38210 SHA256: 39ea39455d410f8ea22430afc9dd96e3c13e18477d8de40882a047f38cae80be SHA1: 420e210ed2362c5864d5382d4ce86fc395fb3b39 MD5sum: 9cda3729eb71f938f04307cd9fedd4e5 Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Package: python3-dateutil Version: 2.0+dfsg1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 208 Depends: neurodebian-popularity-contest, python3 (>= 3.1.3-13~), tzdata Homepage: http://labix.org/python-dateutil Priority: optional Section: python Filename: pool/main/p/python3-dateutil/python3-dateutil_2.0+dfsg1-1~nd11.10+1_all.deb Size: 49692 SHA256: 2cea0e3f06dc717e5e11a088c4b0626e8ac4e7bcb7dc3c111065f9779cb9f4a7 SHA1: 48adb415809dc18fcbe0895606d6354e18ed0dc5 MD5sum: 9802ffb6f93ca6c9f08187f560f3fa1c Description: powerful extensions to the standard datetime module in Python 3 The dateutil package extends the standard datetime module with: . * computing of relative deltas (next month, next year, next Monday, last week of month, etc); * computing of relative deltas between two given date and/or datetime objects * computing of dates based on very flexible recurrence rules, using a superset of the iCalendar specification. Parsing of RFC strings is supported as well. * generic parsing of dates in almost any string format * timezone (tzinfo) implementations for tzfile(5) format files (/etc/localtime, /usr/share/zoneinfo, etc), TZ environment string (in all known formats), iCalendar format files, given ranges (with help from relative deltas), local machine timezone, fixed offset timezone, UTC timezone * computing of Easter Sunday dates for any given year, using Western, Orthodox or Julian algorithms Package: python3-tz Source: python-tz Version: 2012c-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 144 Depends: neurodebian-popularity-contest, tzdata, python3 (>= 3.1.3-13~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd11.10+1_all.deb Size: 31104 SHA256: 3f3bf78b94eeab48a0a1b1265b8ad975a28730adea169278b6a2538fa2b33d84 SHA1: 7a9bc9136a6b3f2563077f4cedc0d1e76ed542b9 MD5sum: 3f265f316cfb72b39827f91dfca95fb3 Description: Python3 version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). . This package contains the Python 3 version of the library. Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd11.10+1 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. Package: testkraut Version: 0.0.1-1~nd11.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 484 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd11.10+1_all.deb Size: 83450 SHA256: ba07d8a04ddb92fd46e907381bf1da22a1be781d5786cf792b447335f275a2ce SHA1: 19907aab2aea4a01e04627b87775fb6b8cc7c5f6 MD5sum: f32ee8d927adb263b85540ce29b1ac64 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.6, 2.7