Package: condor-doc Source: condor Version: 7.8.7~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6156 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~nd+1_all.deb Size: 1334792 SHA256: 89325ff253f0f96e416df60608b2d3630b98237b9c7eb54f94b2f1d028420aab SHA1: 5a6e288f6168c1a624fdc1f83282fbc1a56b7dcb MD5sum: d2195e0034d5a453e684b604935eba5a 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1578 Depends: neurodebian-popularity-contest, python (<< 2.8), 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~nd+1_all.deb Size: 1356156 SHA256: 434aff9b028c4333df4aff71cc45e6b82a98574f6297ddab70d0ebc260ff5e6a SHA1: 5dc49f902c6d89fd0fea7758ce53c9462ec73db4 MD5sum: a9b946a201ad29742748d1c152b6fd57 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: 0.8.10-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 402 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.10-1~nd+1_all.deb Size: 134728 SHA256: 19e78691640b57fe21f9fa9e17ec2e63732069e271e908cbbc580ba9546a5275 SHA1: 833d3349d5dff1930a3499316142846cea854452 MD5sum: 444e19660064b1f5f8771400ff8426af 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 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~nd+1_all.deb Size: 928 SHA256: 05f29073e2746666e0fbc4c111150ef67a4ea5a91974f3d43fb41e2bb16e60ff SHA1: 54dbd1897168ea82b4c803ac9cdf3185b1e1c24f MD5sum: 7a7f53cc7987a2216d7d16bf3b5f0ca9 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 380 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~nd+1_all.deb Size: 296942 SHA256: f1e328eeb5e8aa8cde083ce4c3b1e115f911921a7165f6719cc109685e5fa76f SHA1: 368c7818e51c28f1d98d426a4e687df0b70a0d32 MD5sum: 1b586f2ac96616824bb815a6b8addf7f 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: 4.0.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd+1_all.deb Size: 2346520 SHA256: 00ec15002eb3332a7725e068e08040b8f6ee213a5865c40d8048b1d61ee0ad31 SHA1: 8f573d168f4be5999da86816126368b160207ea4 MD5sum: 23e9540f3d9063363c90cc184e4c9037 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gmsl Version: 1.1.3-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.3-2~nd+1_all.deb Size: 16298 SHA256: 8b2904362e0778cc8f4e98a8434b82370761a477140799a4a89c145880c2d93a SHA1: 6b04296822aa2802032d4f5ec39a566406cc1956 MD5sum: 8d01d62e7d421d0087e635e1cc5a3918 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Version: 0.6.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 302 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~nd+1_all.deb Size: 277608 SHA256: ada9b621c7f57e36ff8722e8b187fb30cbf293fe6fd7ecf8715baeac88239e81 SHA1: 6359e73297b027d6210692e05b9c69eabca20070 MD5sum: 3d9919419c971cf623b5f0ec1832115a 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd+1_all.deb Size: 5164 SHA256: b13a9f5de4bb0b00a2711a3d003a09d9595f5c9ab3734d3f01ce9dbd00732c25 SHA1: aa4e0a55a61260617777ccb2b5c96e072d300524 MD5sum: 13cb30f66b360e09322236cb119d69f6 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: impressive Version: 0.10.3+svn61-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2), perl Recommends: pdftk Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.3+svn61-1~nd+1_all.deb Size: 155578 SHA256: 1a949f934904f75ed7423eadf6adbb8681eeb8385cde68b0c9b99c6292bccde8 SHA1: 8bc934874588d2c5198b574aa249dc62dffcd0df MD5sum: 9f659deb1fa3a18a2c9fab173e4f6e46 Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd+1_all.deb Size: 9644 SHA256: 0d13ef08a008124bb9da089c6b0ee0b6786334ccc1f455d0fbf23dc513dd40df SHA1: bc8cb6cadf98cc14994f98441238f49353e3a04c MD5sum: 4ea2b3f0bbadd9c29c191cb08ac94709 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: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.2.1-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2677 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.2.1-2~nd+1_all.deb Size: 2408052 SHA256: 87a7a9d2d23a62eb09d7bc3cee5fe711315750b90c2fbd0a56904fb1c8eb4044 SHA1: 739a8da4cc45306113be264af825dc75ca93a8d1 MD5sum: 24cc64153345b88410e1979e04bfd7c8 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4808 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.6.6-7~), 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_0.13.2-1~nd+1_all.deb Size: 1306542 SHA256: 33fc418d5aa20d8ed5764ba27113cf8b7dfd6e161f925ce1b3bf179bf11fb31c SHA1: 39a91cecc912b7b453902b3746d62849a55e0b52 MD5sum: 9f74e872ca8b460a4350a340d804f98b 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: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16672 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd+1_all.deb Size: 7241256 SHA256: a0d2235483d3300b6213c473d783db7e62487249a9bed0d418dbad6d44693be8 SHA1: 947c3033758d7974461bd0758e0d765af1750dc6 MD5sum: c52d4bb47c6d7375400b74e24c2f9fd8 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: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd+1_all.deb Size: 900 SHA256: 610ebc3a7bcc05bd450b367852ab26dad5c2f8b668e000fc73880a07cdffda2f SHA1: c722a2a679e2931d56dc828366f160165f16d9d0 MD5sum: 9e7976ee869362eaa11884d2e1ff00c0 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: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd+1_all.deb Size: 828 SHA256: 1f0ce5d07095b2529a2f935f8715d834215d6c4f016179fc33ba6b11dd5855f7 SHA1: e2194703b6e2426cbb732cc34070157910c6c0ca MD5sum: fe3fa702999665bd8751b4daf6afb0d2 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: 0.13.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd+1_all.deb Size: 914 SHA256: ed093f1f0751c34cedd9910b094deecb52e3a7d8865119c5de7d47996acdea7b SHA1: 4aebbc9ff1ce980725e419f8754294d3ea472dc6 MD5sum: 96a49d95cc8ce5bff8422fb5d3f378ba 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 482 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd+1_all.deb Size: 90816 SHA256: 6f15ee9429e9a77208075aa20ac716d28777bb6ae56b6d928d2394f8eba532f5 SHA1: f983624268cf3366f85733d527b08deb8581e0be MD5sum: 812834b439f4a80838af58f2d5ecacc4 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39512 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~nd+1_all.deb Size: 4548630 SHA256: abb04de259b60adb7bbc5b69f0b0fad4c09bd10c212cbc26d553d4f8aa75a961 SHA1: 037960e003e5cbae1196d8ab8408c97ceb18f49c MD5sum: 5936c0eea7bfdd1dea821564ee55f4aa 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 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~nd+1_all.deb Size: 7214 SHA256: d4d5ca7dc4bfd09a284a4ed1ea966794e87f6641586f54cb291dee692f6d814f SHA1: b34327f74bc8d05af20a16ec6eb2f848f52513db MD5sum: e3a2a5ca34fb4be3f17c2b16b7d846f8 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: neurodebian-desktop Source: neurodebian Version: 0.31~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 142 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_0.31~nd+1_all.deb Size: 115304 SHA256: 3ea169032274d9b2f94d4194234a708c789962967e8701c2ef1ea38003b1433c SHA1: 24019a7b378352fe3f94d405b88aa9caa2eb8626 MD5sum: cf7a5026823724fb9d7f28acfd5ed91a 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: 0.31~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5762 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_0.31~nd+1_all.deb Size: 5351182 SHA256: cdd74337dfcaa7534aabc4a0b6b7e9705515bb961614fb15664a49f8c89735d2 SHA1: 3395ed30fe096a6df90892c7b43935d1da868751 MD5sum: b015405654080c1b74a73f37921a4603 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: 0.31~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 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_0.31~nd+1_all.deb Size: 15162 SHA256: 4d8f79d9661824550bc7aa0583f78ad8a17f796064db331af1d7a8af7586d604 SHA1: 04e607fedf547820d9d092511ee38f027bbb9041 MD5sum: 7b81d4bdc823b5088a56c5c2e228baa8 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: 0.31~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.31~nd+1_all.deb Size: 7480 SHA256: 120a046872a5b877693e803105c5739ba1c1190ce9114449cc82c68baf5805e1 SHA1: e5a353e23951d468223fbf5bcdf3c5783e89235e MD5sum: 509007a19f3ab8a450b5f0a67e5735a8 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: 0.31~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.31~nd+1_all.deb Size: 6698 SHA256: 0ba9396373f0d9d3ab26c5bf88ce380f62093e3ff4f368270d6b4ff47f6861e6 SHA1: 880863ab70b75075fa34b24cd1ba8748b3153784 MD5sum: 48f35d9484809d247a15527d1a914fac 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: 0.4.6-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.6-1~nd+1_all.deb Size: 615122 SHA256: a5b8a3053877d7ae8ab4e4008b95ea763d88748a58efc660d84e94cc771189db SHA1: 528f73efa892f9198a102951dbff823c6ce9b9ef MD5sum: 7ec337d5ec0af8ce9560c9ee86b44860 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: 0.4.4+ds-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1754 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.6.6-7~) 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_0.4.4+ds-1~nd+1_all.deb Size: 425322 SHA256: 7d66e788758b64cc4665f5dc6e94eaefa1d3e09771cb629373f1d2a156e838ae SHA1: 0f6fd8ab4d2a1f6165e89a83d150aa2af0001a22 MD5sum: 4d366c8e95b97e5194b5b8da0e6dccde 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 at all. Instead they can be used in the same way as pure Python objects. Package: opensesame Version: 0.27.2-4~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25510 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), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.2-4~nd+1_all.deb Size: 24408556 SHA256: 3bde8f9ab35d6fd5078ef9ab1a261be8cea37da5865f5e39a5daa078a7697ab9 SHA1: b4c254c5f07f15992a4d628da0a2cb22e409fd63 MD5sum: 855cdb546813697a30ab7d092475067a 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9328 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~nd+1_all.deb Size: 2024448 SHA256: 577686111aba7c2eafbe4c25ab1052d26958bd64471319884a324b9825c07d16 SHA1: 2ab042fb217b2ffec6f55b478cf1e8405786832a MD5sum: 8d7802d259052ef28a0dd2febd7fa4e9 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: 0.8~nd0 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0_all.deb Size: 1488332 SHA256: 491bc5917f698fee06888998e8a295a6caac2950148bb160b457aff72437eadb SHA1: c5d75d04b01f681ead660ce8d8fe068ab887fba0 MD5sum: 8fbf7c362fd4091a78c50404eb694402 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: 1.77.00.dfsg-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17755 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, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.77.00.dfsg-1~nd+1_all.deb Size: 14525116 SHA256: 0740379b1da33466e539f4c88627dcc7cc2d42801d947e43acea0836cc83822b SHA1: 160c6d5c88884edfa8efe20ce7c1d1906a67a5fc MD5sum: 365e515dc6e6499c4c30004265040fdd Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.10.20130114.dfsg1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48860 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~nd+1_all.deb Size: 19678508 SHA256: 14fb84e2eb3f29d634aad0fece4cc2391607af5f983de08f6394cb21ddd4c337 SHA1: 01c692fe83ec176e75949bd0e743a2dd9647fb8b MD5sum: d9d72adfcbc1348f869b7671e7afc235 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: 1.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd+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_1.4.1-1~nd+1_all.deb Size: 549162 SHA256: c51f0470d85262e3557539b32304455d5bb809a9b1ddac52613abe40a24a7957 SHA1: 7ec1c4157a18c0015dd7365552bd05c66291b9e6 MD5sum: 7ce8090a3b2209003d5808ec167651b2 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: 1.4.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6808 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_1.4.1-1~nd+1_all.deb Size: 2246624 SHA256: 38ef682cec0640e71c12d12333e41042744720d47394f2a0dc23f45538b8a74a SHA1: 026b4455f186e745bafe42f6b2bc2ec4452acfc5 MD5sum: 96cd6c1886d42aaf452772f7a8fc3dd7 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1818 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), 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~nd+1_all.deb Size: 425124 SHA256: 1671e6de831bede03800f3d83ed7ed3e121e4ebd6744e079eec9b0f065910925 SHA1: fe9f19fa102c66c737bb921e2166de3a63f09845 MD5sum: 37888fb0e3cda0564364af8723e4f079 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-dipy Source: dipy Version: 0.6.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2286 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy, python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.6.0-1~nd+1_all.deb Size: 1588498 SHA256: 95015a6bc1f67e0d680918d722022d3962a6c4056cde3312966c1d31f68bdf49 SHA1: 1a99a4abbac3bcceaa16dd5eed2e1deea56f1ac8 MD5sum: d0134cde725aca9d0e6209bd3efe9856 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.6, 2.7 Package: python-dipy-doc Source: dipy Version: 0.6.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5081 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.6.0-1~nd+1_all.deb Size: 3616888 SHA256: 298c90c30b365046a409463a1a4e9cc8edde70ee96524bcf457698abe4ca31ad SHA1: 89fd0631e25f4e653abc311b634f412f8858e926 MD5sum: b32b6efc30426b97f3c3f518449a606c Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-joblib Source: joblib Version: 0.7.0+git14-g3e5784c-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 182 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_0.7.0+git14-g3e5784c-1~nd+1_all.deb Size: 54640 SHA256: 0501c02e311e0f5d8ce13827a25144377026c1e9f13c8cf914b25e0c936a39d4 SHA1: 01f5c43f9de5c1350fa2c2f8fbfdd64c3f24cc75 MD5sum: d6f59744e67282e0c705308f88d1381f 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), 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~nd+1_all.deb Size: 7328 SHA256: dbb35c5dc374c7bc62e95a56d3a14314105025852a66ba61f2472e4ea5b8be65 SHA1: a7bdc4dd42a3963a810fe0c0e73e4ad7ed6a7995 MD5sum: 1d204a47646dc6ed4152895171c87bc9 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1528 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), 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~nd+1_all.deb Size: 484096 SHA256: d82dbf8a5b5738ebdf22bd138714552c8332ed81a3d3ce7a15e63e96f203b214 SHA1: 8577b8829def8e006d3c14b6b4011669c9140d4f MD5sum: 41627718c5a1f5de89c8ce8e12c404b8 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-mpi4py-doc Source: mpi4py Version: 1.3+hg20120611-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 284 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3+hg20120611-2~nd+1_all.deb Size: 79404 SHA256: 3a4a4f43f2b5e861c2ae1d20f8b3c020a561dc32913fe7c924df268e9b1db993 SHA1: 846423f647b475cbd74e2cf6de2d157cedbd58e5 MD5sum: 72a7fb53e1d77d4c6ab17aab7c8eecd8 Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd+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~nd+1_all.deb Size: 2205002 SHA256: 41465c88b5c5d855bb5cfb183ef31b621031eb691ba5a8f3ac481bec2fe61bd8 SHA1: 40e31da97e30b6c2af3f28dfcd4b255560f765e2 MD5sum: b36ff1ec87893ae209624c75e8934b87 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37565 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~nd+1_all.deb Size: 8454400 SHA256: 9f730cbbc6fdcfce45ecca5ef036d74ea074eaedf2b4105fde7baf0028f11350 SHA1: 4510a24072100ffb1d4220f2d66d21abde733b9d MD5sum: 32c7629e7f9e01d9f7ca4d2c621b85be 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: 2.2.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4241 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-3~nd+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_2.2.0-3~nd+1_all.deb Size: 2400380 SHA256: 6a98d6ca684dd9e0dcb06e96bbafdc3191fd3383cd024bff7d02b4e8bd6e261c SHA1: 91e85bf840fd29e8174dee917718154d458485c0 MD5sum: 1f9c6d453a4fb0f1816cba858506a077 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: 2.2.0-3~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17238 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_2.2.0-3~nd+1_all.deb Size: 5158000 SHA256: ce50706ee99ac35f2e25c4c6ef25fb1064449444d1127b3cfb798d9d98ee16a1 SHA1: b6ef596b6738a65ac158ef50136560ff651c98cf MD5sum: 097c6797be1902f5366c6e1d1220abd1 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: 0.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2451 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.0-1~nd+1_all.deb Size: 1433858 SHA256: ecf7aed8d22553fa8f72aac1bc13763d3e2aff3a421f6dc2f32bfbf75e02db90 SHA1: 000c8bd0d19fbd7a417ed3f128aacb261cf2440f MD5sum: 2e4c680f05d3b3214f13fa5b537bc192 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-neurosynth Source: neurosynth Version: 0.3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd+1_all.deb Size: 32506 SHA256: 1b7a6109b4cd73ca4ed17d0f33010df1d73c8bfcdd469311c48d58714fd99755 SHA1: 101a6b891d69881db9e5bd6bfc176aaa2de3ca28 MD5sum: 191a7178983f856cb47820d478a3a791 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4159 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~nd+1_all.deb Size: 1826550 SHA256: da75dca6c3f18abbcf2002e7d6f9431cdc0a637a5ed16bb8c6cb9f6e5618b4ef SHA1: 85f6cbb660c7dabcde1a35f1da2cd017a9c91001 MD5sum: 9da5139ccc2bf80df0c39c79d31d39d8 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2446 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~nd+1_all.deb Size: 448186 SHA256: ed6dcae3cb79e05b8c1b336ce4eb2dc5479288c1aa69c1c321a69033a22f2ae3 SHA1: 719894fd05fe60017dad91079d1e6d972f9a398e MD5sum: 84ba334286af8bf241c85739d659e6f0 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: 0.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2865 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0-1~nd+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_0.3.0-1~nd+1_all.deb Size: 785860 SHA256: f02f5b7e0d62168b495c371ddbf87b8378f7f67bc60b70297a9aa78d353e8500 SHA1: 9311f209de048e3167e7f99a8ec2653a2a943197 MD5sum: bddf885165aea270af36d814965100dc 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: 0.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10296 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_0.3.0-1~nd+1_all.deb Size: 3763100 SHA256: 91c3278a29832c96a59450fa4f51e8ff9087e5cf96a1c5955071a1a6a89a12de SHA1: f372bc014b24ae5140698255435856002cbfa91f MD5sum: e9dc281c91fe803e10306abc73713484 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: 0.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2657 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.8-1~nd+1_all.deb Size: 591930 SHA256: 26489fca0d1174f0400d824a0fd036fa0bd16243094803969936965bb8a1c7a7 SHA1: 3d208f8637599f4f7d196743d72566f9b5c8d764 MD5sum: 1351fe3fd3a5dc4e52446d0bf082d16a 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: 0.8-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14896 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_0.8-1~nd+1_all.deb Size: 7031366 SHA256: 8b9898ce668bf26fff0b2450036fd7266538f731d26588d4f4fcdf34256a871d SHA1: a0434778b00d6f847e7188f9e5ddc4c294b21d25 MD5sum: ac55dc22f1cfe82c6cb06bf9382c3dbc 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9294 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~nd+1_all.deb Size: 3908880 SHA256: 9b84b1e4c4790ccc493da1e3bf4821527daeeea31373bc9ad826ca06310b069d SHA1: 6cd41f955262beada04f56a20e76bec57566a89d MD5sum: 39ba98d1a26fb31da6f01dd7e2158aa9 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6842 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~nd+1_all.deb Size: 5338022 SHA256: 0898b98c18494ae1229649b0999f9c4d7a3d5f4879ca9ba759b5f003559b79f3 SHA1: 96d08bb174a9d70283889f9958b7a3d843036720 MD5sum: e3a376c097907d751641e5f0a77e5400 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 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~nd+1_all.deb Size: 245088 SHA256: 720267e7fc1297916d72081d7bffedfc4e911f4cba267f9e83f65ee6cf7eac3b SHA1: 2a31c5c6bad612fa5d880b23d6c2c2628c1aef20 MD5sum: 5ffcdd148bf0a2e648d7c3960953fc20 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 291 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~nd+1_all.deb Size: 62016 SHA256: d1d387a156f6f40626b291cf686a492d5fae862a94d535b902b67f123a174096 SHA1: 5fcc073bfcaa4259fcef963d468f902d49dc3df3 MD5sum: e1665357b5e9793f8ec360072393bbea 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: 0.11.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4672 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.11.0-1~nd+1) Recommends: python-scipy, python-matplotlib, python-tables, python-xlrd, python-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_0.11.0-1~nd+1_all.deb Size: 953484 SHA256: 61bc3c7a8481e880efef6aa2cb959700063ac0c9c10cf7b2536c8358758d1e1d SHA1: 479c2aa1e7b174549028ed4cc3bdeeb91ac32ebc MD5sum: 85b8dc59dd9e5f044007bd9902f0d609 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 . This package contains the Python 2 version. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 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~nd+1_all.deb Size: 34266 SHA256: 6ef3aa699e927edfc8ec788d98beac10bec4de47387f92d79d44b0183b3c3c3d SHA1: d8942b2e7dddc5e29cc52b2a4cca69a392348a09 MD5sum: 36530320f9038b882b0e8b9d5be61505 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 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~nd+1_all.deb Size: 21334 SHA256: 3ab479e9d42286158d724eb219d6205e3c8071a2a8fd6436afc501b57ecf086b SHA1: 19b81597aeb2806a30580c43b1fcf5f5ad3d586d MD5sum: 662336ec73a1d4c272a6d2763ef118df 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-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-1~nd+1_all.deb Size: 818156 SHA256: 3c9ca00124455d4c53fa4c851d5b487d3365b9d94e9663bf666fd27073f37c0b SHA1: 7d8097eba90d9ba5ddd134cd9735ce971ede6f8c MD5sum: 0f2eddf510110a837eaee61e6a574699 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pymc-doc Source: pymc Version: 2.2+ds-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1840 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.2+ds-1~nd+1_all.deb Size: 903860 SHA256: 49c91294d31287e3ca2606e0cc3bef7182e9276130bd6d2805e5edd03c91e6ea SHA1: 48be75f9daca7b5d4277fdd5c48382aee76c7113 MD5sum: 4d31a5b8b73523612b42ce1cb1d9c0f4 Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pynn Source: pynn Version: 0.7.5-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 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~nd+1_all.deb Size: 192126 SHA256: 4c25bbb4a6efbe9c9614c69977d255840ceb5469e917e9e42631963e11fa73b5 SHA1: 4d68ad72d900c06b6d482eaabab6847de44296f2 MD5sum: bb6a423667c3716a6baee943a4d73d50 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1722 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd+1_all.deb Size: 376516 SHA256: c44e63035749ea2429ed9f2aab12e3ce41aa533de51f0cf3ce9836f882e3a477 SHA1: 2baec2a6b0c311f22f1a6e685cea20ae7019dac7 MD5sum: 603bfa4501f120f7ca01ed5ed95293b0 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), 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~nd+1_all.deb Size: 62610 SHA256: 24764ab44e8e2357cdb8d4882acce352d96b34ed6b3af8be217617eb51848f83 SHA1: 9367905e8af4cb696831c327b20e43a3e2d52616 MD5sum: d08b442a214c35f1e1f9fa595d311cf6 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: 0.13.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31 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_0.13.1-1~nd+1_all.deb Size: 28616 SHA256: cdbdf4d9d281d741d83dae5123dbb7a0bbe012fb30affb23f112548fb5124041 SHA1: 7ed7e61501a8963a54a9e34de1b36b2a1379cff1 MD5sum: 52a7f0afbee33265c928f8aeace6054f 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-skimage Source: skimage Version: 0.8.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4550 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.7, python-scipy (>= 0.10), python-skimage-lib (>= 0.8.2-1~nd+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging, python-qt4 Suggests: python-skimage-doc, python-opencv Provides: python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.8.2-1~nd+1_all.deb Size: 3236944 SHA256: 9ccf698b2f86a5517458862eea37073b41ee99dcb059d51bb87abab0cbebef66 SHA1: 4e2bd8d0265b7a4ca861ca50eeff5b79459c4971 MD5sum: 2662b15c327e0fc2b6a27999a7c37b0f 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: 0.8.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14205 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_0.8.2-1~nd+1_all.deb Size: 11825154 SHA256: 90c7e19ce6211a6f75051a17f921b156fed774aee631e90e5034f51bd67b54f0 SHA1: 50c1fc8dd826c40d3ab60da7625a9ccaef3dfe75 MD5sum: faf9515cbc1420004908b44afb348d66 Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.13.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3050 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.13.1-1~nd+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.13.1-1~nd+1_all.deb Size: 1012752 SHA256: c74e61d90c6793837306ea2f54245512639e70fb7fc4cb079fede9aef15eb47d SHA1: b456ea56fde1155ac7a33bdf83c9e23aa6c69a3d MD5sum: 77132bc53de23e9395177cd77c10cba2 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) Package: python-sklearn-doc Source: scikit-learn Version: 0.13.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42499 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_0.13.1-1~nd+1_all.deb Size: 31111726 SHA256: 34207bcd140d5056a070ad68b019464d1182a7858ed5424c147ea81f22e7cb11 SHA1: 4e717222ecf3a38c392d1cadd1454240496f043f MD5sum: 81359295892fcafe77d657ec6393198d Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-spykeutils Source: spykeutils Version: 0.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1976 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.3.0-1~nd+1_all.deb Size: 392004 SHA256: 2dd15d5024fe95d460932e087514b99125d29dcbd5b3f808f2dd04b031a5f41f SHA1: 96ba5becbb05b61c2076a0a4fb4a8a7333eca998 MD5sum: 9ebe6f5284953019663301547d9e39c2 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.4.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12433 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.2-1~nd+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_0.4.2-1~nd+1_all.deb Size: 3104792 SHA256: a81091e1b48cd71e93c566a120872cd491c9fa0a8ca9ed9421a28c779f05f8f4 SHA1: 7ce0061bc0757b101051605b015f9a4754944c38 MD5sum: e2fe656c2074d522e7cecde0d4b5fda3 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: 0.4.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23647 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_0.4.2-1~nd+1_all.deb Size: 7342598 SHA256: dacbb7d4e0cbcac0de0fb1d541ab55c2c4e3c21cc65aaf3b999527f0ace6efb8 SHA1: b313e92edeeeadd3992c44c76d4e4ea37c26792d MD5sum: 43176138c31ec98297cd792d03604bde 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~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 95 Depends: neurodebian-popularity-contest, python (<< 2.8), 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~nd+1_all.deb Size: 28904 SHA256: bf545c8b3b5ece2227156e364694d07e7df8b5c2a98cc662c61077e70539e87d SHA1: 096d69ae640c7a9dbdbd958e260da4745308ac9e MD5sum: e49aa156e4c8f1ab8f34630c44d82bbb 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: python3-pandas Source: pandas Version: 0.11.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4567 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.11.0-1~nd+1) Recommends: python3-scipy, python3-matplotlib, python3-tables Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.11.0-1~nd+1_all.deb Size: 943390 SHA256: 8bf2522394e6511693c57a20b5f77b0a47ea45042aaae9e9f1a4c954cb147397 SHA1: 267ed9f9996df74c2b785bf0c02d0238c0a85e6e MD5sum: eeba5041806c8223081eeba1139be196 Description: data structures for "relational" or "labeled" data - Python 3 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 . This package contains the Python 3 version. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18626 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd+1_all.deb Size: 10737524 SHA256: b6a893c9b80b40421f5d12d9a135bdc12fb17f3fab59e0106ef1fc24ad3e77af SHA1: 235814d62d21157760fbae3cf4401cc6d48cf555 MD5sum: 33cd04a3593f6f216ac68a3fc0ea82a4 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: 8.5236~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73046 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd+1_all.deb Size: 52180218 SHA256: 92b31d00b8ee13b7bcdf249cff509ec988cc2fc0703e301eed662571b89135f3 SHA1: 6540ca3feacd1d7efd53733b78fde41c7defb2c2 MD5sum: 973ce7224b20331a4ccfde26eba8acbe 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: 8.5236~dfsg.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9273 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd+1_all.deb Size: 8991072 SHA256: a10f23addd5b16acad8feabb200ab8fda604f9ea177e7c32053ab0c60a768d9b SHA1: 65a715c185007eaccc71513dcad03da43d23cbdc MD5sum: 890e9a307f742f1573165ce80a18032f 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: spykeviewer Version: 0.3.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 932 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.3.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.3.0-1~nd+1_all.deb Size: 490506 SHA256: a50253a21a1334e09122b5911ce8503c3498fdc1d7048f2db70ec7c7cdfb128d SHA1: ca6cdfcfebdcf18e3b2e702aadb9629ad76237cf MD5sum: e90ce233dad05d1878b443ae81260ed8 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: testkraut Version: 0.0.1-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 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~nd+1_all.deb Size: 100016 SHA256: 5ea3d436c473902040c138cb6100770fc3d0969e891ce84eabf2f644ee367a5a SHA1: b29570908455d37b7397bd9eed303b13e59b090b MD5sum: eafa9781d2a05681ca03188015f96302 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 Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.2-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.2-1~nd+1_all.deb Size: 50202260 SHA256: e7ec09f8102fcab04c6770c2136fef9e2d345493be608483ee2780e70833fe92 SHA1: 5b57f36387c567517d677ac76ff46ce05c2a1d1a MD5sum: 93f49c7c1aa6f545ac87e214c7f30af8 Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit. Package: vtk-doc Source: vtk Version: 5.8.0-7+b0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 342007 Depends: neurodebian-popularity-contest, doc-base Suggests: libvtk5-dev, vtk-examples, vtkdata Homepage: http://www.vtk.org/ Priority: optional Section: doc Filename: pool/main/v/vtk/vtk-doc_5.8.0-7+b0~nd+1_all.deb Size: 66710216 SHA256: ef2921e37681f7364119b79457483cd3ca7da8cd063a96438cffe23aeba52938 SHA1: abc4b1ccf35fd6c0cc20f67836fb7ffcbfc69161 MD5sum: b7ef2d7972fe60ad7ce2f891faac4205 Description: VTK class reference documentation The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains exhaustive HTML documentation for the all the documented VTK C++ classes. The documentation was generated using doxygen and some excellent perl scripts from Sebastien Barre et. al. Please read the README.docs in /usr/share/doc/vtk-doc/ for details. The documentation is available under /usr/share/doc/vtk/html. Package: vtk-examples Source: vtk Version: 5.8.0-7+b0~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2521 Depends: neurodebian-popularity-contest Suggests: libvtk5-dev, tcl-vtk, python-vtk, vtk-doc, python, tclsh, libqt4-dev Homepage: http://www.vtk.org/ Priority: optional Section: graphics Filename: pool/main/v/vtk/vtk-examples_5.8.0-7+b0~nd+1_all.deb Size: 578892 SHA256: fab181213376a1077411077e48a5640af76ceb2868302e2e03b18e4e6a0859fd SHA1: e0087beef829cbfd4d09abfd52a4e526b2b11963 MD5sum: efe0f5b35bccb7b5f9d251a25970a0ac Description: C++, Tcl and Python example programs/scripts for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains examples from the VTK source. To compile the C++ examples you will need to install the vtk-dev package as well. Some of them require the libqt4-dev package. . The Python and Tcl examples can be run with the corresponding packages (python-vtk, tcl-vtk).