Package: arno-iptables-firewall Version: 1.9.2.k-3~squeeze.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 844 Depends: iptables (>= 1.2.11), gawk, debconf (>= 1.3.22) | cdebconf (>= 0.43), debconf (>= 0.5) | debconf-2.0, iproute Recommends: lynx, dnsutils Homepage: http://rocky.eld.leidenuniv.nl/ Priority: optional Section: net Filename: pool/main/a/arno-iptables-firewall/arno-iptables-firewall_1.9.2.k-3~squeeze.nd1_all.deb Size: 132476 SHA256: b002efbc460e228ef300147169187793cc9cc8b36e7acf807567d35aa8d56099 SHA1: 7945add5a3b0968d8deeac27bb6d5bdf667ff03a MD5sum: ebcb9a6d4f275258f76616360ff739d0 Description: single- and multi-homed firewall script with DSL/ADSL support Unlike other lean iptables frontends in Debian, arno-iptables-firewall will setup and load a secure, restrictive firewall by just asking a few question. This includes configuring internal networks for internet access via NAT and potential network services (e.g. http or ssh). . However, it is in no way restricted to this simple setup. Some catch words of additional features, that can be enabled in the well documented configuration file are: DSL/ADSL, Port forwarding, DMZ's, portscan detection, MAC address filtering. Package: autotools-dev Version: 20100122.1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 216 Depends: neurodebian-popularity-contest Enhances: cdbs, debhelper Homepage: http://savannah.gnu.org/projects/config/ Priority: optional Section: devel Filename: pool/main/a/autotools-dev/autotools-dev_20100122.1~nd60+1_all.deb Size: 72966 SHA256: dee3f923f4e6856aac8efa5aa8c890af4466679721b9a2dd03977c7bddf0d857 SHA1: 2c2a0419c7324111348c91772971ffef898ef835 MD5sum: eab0255d3b1d7620acccb2f6e01b667e Description: Update infrastructure for config.{guess,sub} files This package installs an up-to-date version of config.guess and config.sub, used by the automake and libtool packages. It provides the canonical copy of those files for other packages as well. . It also documents in /usr/share/doc/autotools-dev/README.Debian.gz best practices and guidelines for using autoconf, automake and friends on Debian packages. This is a must-read for any developers packaging software that uses the GNU autotools, or GNU gettext. . Additionally this package provides seamless integration into Debhelper or CDBS, allowing maintainers to easily update config.{guess,sub} files in their packages. Package: condor-doc Source: condor Version: 7.8.1~dfsg.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6916 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.1~dfsg.1-1~nd60+1_all.deb Size: 1320658 SHA256: 8ebd4d6776ba04c98c28056124623d719a8a35acb625e407ee8cca475295af2f SHA1: a78938c20dd5276404b8b48f86320eb717326353 MD5sum: b5b6c6f0557cf00c590183b80859ce67 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: 2.0.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1884 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2, 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_2.0.0-1~nd60+1_all.deb Size: 1354956 SHA256: b0950f7c42d584f3476f79920cdbfcc342d10563f1b06b88acaab7263c36add6 SHA1: a713af7f9f16e3b54e22916ee6498bdaafebd798 MD5sum: 02d405b1f02ad49b4c2192af7ee48f1b 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: coop-computing-tools-doc Source: cctools Version: 3.4.2-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2676 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd60+1_all.deb Size: 303978 SHA256: 02f046f23d55becc2755a8ca914586b2de0d9bf606ed29a3575b3194e7b2eb5c SHA1: 9ad253f98144263653d52f26bdf39864f45c1dd6 MD5sum: c7af39ed432c13e3360d6f131d4dcccb Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: 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: debootstrap Version: 1.0.26+squeeze1+nd2 Architecture: all Maintainer: Debian Install System Team Installed-Size: 228 Depends: wget Recommends: gnupg Priority: extra Section: admin Filename: pool/main/d/debootstrap/debootstrap_1.0.26+squeeze1+nd2_all.deb Size: 57968 SHA256: a67ca5bb752abb8a685119181a8441adaf618c73c6d908d23e1d0833e5f0b46d SHA1: d0cd11357a327591e2bc76ffb8e81341231adb2f MD5sum: 482fab29e945ecd9ea88e67078bcf209 Description: Bootstrap a basic Debian system debootstrap is used to create a Debian base system from scratch, without requiring the availability of dpkg or apt. It does this by downloading .deb files from a mirror site, and carefully unpacking them into a directory which can eventually be chrooted into. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8144 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd60+1_all.deb Size: 7224698 SHA256: a949ef784b2c7f5ae0b5b9100560fae81c897e84e80867b8c3b8ecfad708d35a SHA1: 2b43e6ccafdcf7014cfcfaf50b0fa9b02c9b501b MD5sum: 352d3cdaa19694e929bf647167979968 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.8.6-3~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 612 Depends: neurodebian-popularity-contest, python (>= 2.4), python-central (>= 0.6.11), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-gamin Suggests: mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.6-3~nd60+1_all.deb Size: 103500 SHA256: 3f363bbb4aab43fe62721452194b2b6d50a314c03822b4f3fba2a64760050edb SHA1: 6a88525f9cead8210d6b8ddb74efb15c205002fa MD5sum: 32b9330132d95ff6eeb12af858302894 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Python-Version: current, >= 2.4 Package: freeipmi Version: 1.1.5-3~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 0 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd60+1_all.deb Size: 928 SHA256: c46f6da38fb90af9be1f3a13bddebd2c247ed78b93eb7033fb0cb59fa64f189b SHA1: 46af53befb674b2134f74df99ea5df35c1f83aee MD5sum: 93a05e907c7ba907a2abe75b4889eb75 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 472 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd60+1_all.deb Size: 296940 SHA256: 0b1b4699ec10c17bdf97f704655bd8057614827c955d79cf4bcc301ec939081a SHA1: 55c1df2a144f8b4ff339f640c82f78984cc8752b MD5sum: 73f665137c1e25e5610e06ce6005e5cc 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: 3.1.8+4.1.9-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3124 Depends: neurodebian-popularity-contest, qt3-assistant Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_3.1.8+4.1.9-1~nd60+1_all.deb Size: 2351302 SHA256: 64c32f8bbbbbfddfc65e97d37eec4341d377874849c6e8f7f759aeae873e7a45 SHA1: 73726555ec7747f07c2bef1b77c62eb10e381e09 MD5sum: 66b10fb39b745b649ba377098763568b Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: guacamole Version: 0.6.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 344 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~nd60+1_all.deb Size: 275606 SHA256: 0c3c73bcda15707f6e644bfbd545966312a267c7c5b8526a28f9412feb497cf9 SHA1: 86bf14641701d194201b262175e13356e46d04d7 MD5sum: d5923438677f531f5c29450191e65c75 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd60+1_all.deb Size: 5174 SHA256: aa8acc1cb1e31f859346456991551b966c472078d0c45ee108c6dba6761791ce SHA1: 9d10b5db60de10ddf46924375abea6bb2f2acf37 MD5sum: c17c7c3a2c972ad60ae57492ecde1ffe Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: ipython01x Version: 0.12.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4692 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python2.6, python (>= 2.6.6-3+squeeze3~), python (<< 2.7) Recommends: python-tornado (>= 2.1.0~), python-zmq, python-matplotlib Suggests: ipython01x-doc, ipython01x-parallel, ipython01x-qtconsole, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.12.1-1~nd60+1_all.deb Size: 932262 SHA256: 80fd7d983cded19944940192449aa06e7825957b8531d07754d8d3b75af60e3f SHA1: 6301e58788ce98263b36da38515e34cc7766f7cc MD5sum: e75675119903e0019801fc9785f64860 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.12.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13852 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.12.1-1~nd60+1_all.deb Size: 4357830 SHA256: 697e81d4b91cfe641c5c50db1e6282206b73e8f60040dd866db94af0910f4607 SHA1: 198a553a7c40cf9ff49d448c0a9df25b8dc69b7b MD5sum: 776d4666112b5e49c40c639e88bda610 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-parallel Source: ipython01x Version: 0.12.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 600 Depends: neurodebian-popularity-contest, ipython01x (= 0.12.1-1~nd60+1), python-zmq (>= 2.1.4), python Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x-parallel_0.12.1-1~nd60+1_all.deb Size: 114660 SHA256: cdf89aeab0e58acb554a7af3c98896a8d6ea5799d259e773a2f804bdf75feefb SHA1: 6c1d12cc4a5120c0bf8edd6bb870824fb08210ad MD5sum: be5caec2f51509f334a7f077118805b8 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the parallel processing facilities. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-qtconsole Source: ipython01x Version: 0.12.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 380 Depends: neurodebian-popularity-contest, ipython01x (= 0.12.1-1~nd60+1), python-pygments, python-qt4, python-zmq (>= 2.0.10.1) Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.12.1-1~nd60+1_all.deb Size: 78794 SHA256: 16bb77bf9ad8bd2f749c13069bf15c44d20e37473b3d6667e2cc1a1188c2624c SHA1: 15ef28709f2a8313ccdd28eac99b12eb77bf154c MD5sum: 779dc731788945a7b21afcfed2b156da Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the qt console. Package: libeigen3-doc Source: eigen3 Version: 3.0.1-1.1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10624 Depends: neurodebian-popularity-contest, ttf-freefont, libjs-jquery Suggests: libeigen3-dev Homepage: http://eigen.tuxfamily.org Priority: extra Section: doc Filename: pool/main/e/eigen3/libeigen3-doc_3.0.1-1.1~nd60+1_all.deb Size: 2644024 SHA256: 695a17eef4aa0e2f79eca25972103ab07407d4cf73bc007b9fb28df0786b347e SHA1: 01875672385364e3b13d603cd81d900cfc1c8c9e MD5sum: d691c4890c6c1d5aeea56244134001af Description: eigen3 API docmentation Eigen 3 is a lightweight C++ template library for vector and matrix math, a.k.a. linear algebra. . This package provides the complete eigen3 API documentation in HTML format. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 576 Depends: neurodebian-popularity-contest Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd60+1_all.deb Size: 88002 SHA256: 7ce4f9208a48ceda67a4206c930d4ef92f12c201f170cd2a2b673ee9bbf8f4c4 SHA1: 7f22cbd72ef2c8d52d0fece584df480966cda29a MD5sum: 00d4f8e36368a75d47c688a18db44f82 Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libisis-core-dev Source: isis Version: 0.4.7-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 344 Depends: neurodebian-popularity-contest, libisis-core0 (>= 0.4.7-1~nd60+1), libisis-core0 (<< 0.4.7-1~nd60+1.1~) Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-core-dev_0.4.7-1~nd60+1_all.deb Size: 68980 SHA256: fe9df025e015264884a7553966d5d637d0be183acca87cba3bc7a4e66bbe5140 SHA1: 74106e21c7e4c06e29fe095ad5fc661e9629ede3 MD5sum: 7f0a04ccd10d5bfcef4f3b1586283416 Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides headers and library to develop applications with ISIS. Package: libisis-qt4-dev Source: isis Version: 0.4.7-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest, libisis-qt4-0 (>= 0.4.7-1~nd60+1), libisis-qt4-0 (<< 0.4.7-1~nd60+1.1~), libqt4-dev Conflicts: isis-qt4-dev Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-qt4-dev_0.4.7-1~nd60+1_all.deb Size: 6022 SHA256: 28e4b5db7773d9fd49e11fc68a958d37734279347da6098e99abb6c6b42e0c91 SHA1: d2680f40e063f20bd0b9a2fe8e15eea731b34237 MD5sum: bd0cf86c411a305c1064b4e84e358b08 Description: Qt4 bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libnifti-doc Source: nifticlib Version: 2.0.0-1~squeeze.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1896 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~squeeze.nd1_all.deb Size: 245414 SHA256: c421052431a49808544394d7242ddbd0437c09c001e9936fa302d29b653603d6 SHA1: 16d20e3475e20aaf39aa4df9231cb5117421d33d MD5sum: 1de8bde7f67f9fd2b7f2571ba0212457 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libopenwalnut1-doc Source: openwalnut Version: 1.2.5-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41228 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.2.5-1~nd60+1_all.deb Size: 4250672 SHA256: 187d9db9af70ee2c4eed8c47e487a7b1d334f0e80b99634efb5f2569c36e7d6f SHA1: f1960a9063e1a4aaa672f01ecde74e0fba3d6c74 MD5sum: f257fecd36b6860f03e27ea4575b8c51 Description: Multi-modal medical and brain data visualization tool. 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: libsvm-java Source: libsvm Version: 3.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libsvm3-java Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm-java_3.0-1~nd60+1_all.deb Size: 13482 SHA256: 747f6bbaa0672dd192c281637bd277fabe9147c7d20168f2b6fd17e20038e3de SHA1: db5548e811b699c6a300814200cf0e949dcce62f MD5sum: d984c74835cf5628722c9688890e79c3 Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. . This package solely provides a symbolic link from svm.jar svm3.jar. Package: libsvm3-java Source: libsvm Version: 3.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, gij | java-gcj-compat | java1-runtime | java2-runtime Suggests: java-virtual-machine Conflicts: libsvm2-java (<= 2.91-2) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm3-java_3.0-1~nd60+1_all.deb Size: 60470 SHA256: c987074f9d3999f640bfcb339c768614ff592d3912a0ed5612b1a7dce443057d SHA1: 3c15a635564faded13725b2e51510f4dfb8cf7cf MD5sum: 9ee4532e7ca8eb5d96ef4c8bd603a7b4 Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. Package: libvia-doc Source: via Version: 2.0.4-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1084 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd60+1_all.deb Size: 115734 SHA256: 91c80bb11eb66c49556d0406b465a4a14585c487049c208e10d38bbc0236fb32 SHA1: 420196944169b914df4234754a5e361bf8f5c6df MD5sum: 74cad029d3563ea0883f26c18cebde66 Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: lipsia-doc Source: lipsia Version: 1.6.0-4~squeeze.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 7004 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/l/lipsia/lipsia-doc_1.6.0-4~squeeze.nd1_all.deb Size: 5539242 SHA256: 698077dd0ec212ab7db8d81fb1ea253fde3176d0817184edf9cc35f1b634be0b SHA1: 9370ec74bf24fddf9143bc0556f7f3535560b929 MD5sum: 5d38c0c06db5d46971b92e261ab545db Description: documentation for LIPSIA Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This package provides the LIPSIA documentation in HTML format. Package: matlab-support-dev Source: matlab-support Version: 0.0.17~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.17~nd60+1_all.deb Size: 6714 SHA256: c9d8769cacb4434c53f934103443e67c2d7fcab602d844981859e19d5502083f SHA1: 7dab897a32b5738abfc1d0f2922dc09ceeafdfe4 MD5sum: 5cd8e52173ced3af71ff2a4fa0f5c598 Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mricron-data Source: mricron Version: 0.20120505.1~dfsg.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1808 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd60+1_all.deb Size: 1666920 SHA256: 56c52d2a111b70c99b690fdeb14c7e81202f77b4a259abfdecd81788d30a1370 SHA1: 1b21cd9b2f70b9eab601b2b3c216e17646f5ce30 MD5sum: fea15f3290f4ea9651165facb5ffb328 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20120505.1~dfsg.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1180 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd60+1_all.deb Size: 738418 SHA256: ee476dc6200ba902147296b9f7ade40c17115e8a516e5bcc15bd4dfd43db09e2 SHA1: 8ab3817abb7fc8d8afed8734c559156e772a6ac5 MD5sum: 11bed3073a647926e4bd1f0f7306eba3 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix-doc Source: mrtrix Version: 0.2.10-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3696 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.10-1~nd60+1_all.deb Size: 3322008 SHA256: b58b506611c0c27969e478e21e2f5d2d36b5add16753675eb7ea3a8c2d2ec7dd SHA1: 387e41f7590acacd1a8aaf63f2df89ee673c1234 MD5sum: c0ec30d6ccdedb91c4b5f5e7ae62de2f Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.28~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 268 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.28~nd60+1_all.deb Size: 113740 SHA256: f1dc191e47399abd87fabc02f287507add242d5277ac8ef7171631569727cf43 SHA1: 3ef45836ab8f26c95ae8a179ab4bd7ab6e5df7d3 MD5sum: c2caf9a9b9764bf9935f9dbf83b11ed7 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.28~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6184 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.28~nd60+1_all.deb Size: 5345828 SHA256: b9bdbceec70273bf8c85c92292ecf730f281dcaf60fc0e869809f4f4ab5a696a SHA1: e12623d9965c5a1c1adafa97c4c5d9146c533cbf MD5sum: 3bb3da9edf78e3f92297eea77a37d4ef Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.28~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | gdm3, update-manager-gnome, update-notifier Recommends: chromium-browser Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.28~nd60+1_all.deb Size: 13604 SHA256: 7594d8170d3b5b228965239550531d590e151ce5a778addd941a5169fc18d2e0 SHA1: 4d2342f9202350b2e5901f5a4c097b193c1a56b6 MD5sum: 478ee9934fddfb75cd849e240f5f5e68 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.28~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.28~nd60+1_all.deb Size: 6550 SHA256: 0b056937862bf05bbd925846e7f4edb7d624c98dd7f0a9ebf282e611c3a8f42e SHA1: bd51ef4f695c229acdaf20cd00bdbc821402f933 MD5sum: 0275ce45ebb0433c57b6eb96d9138946 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.28~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.28~nd60+1_all.deb Size: 5722 SHA256: d444c0f021a2f47e8dd07579aab3c2acf2998480432b6746644a36c05c6bfdfe SHA1: 5f034b906fe4854fb8bba17c382e290f9845edb7 MD5sum: 376bbe8a1924f69788d8a53eacd7d7fb Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.5-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 652 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.5-1~nd60+1_all.deb Size: 614938 SHA256: bcfc65684e71f19ec2f886ca7b51dfbf7deb56eb3a43b446753665a298ad528d SHA1: a4a08b3a0ac24bca70384fbce596474ca278261b MD5sum: 3c4f26fe0434d5b40693624e03ae6350 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: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 36 Depends: python-nibabel (>= 1.0.0), python-nipy (>= 0.1.2+20110114), python-dipy (>= 0.5.0), python-nipype (>= 0.3.3), python-nitime (>= 0.2) Suggests: python-mvpa, psychopy Homepage: http://www.nipy.org Priority: extra Section: python Filename: pool/main/n/nipy-suite/nipy-suite_0.1.0-2_all.deb Size: 3898 SHA256: 882c8580ebd2d458a92f8d851d1ec9291fecf05f6ed98a8b754eb831c95368c8 SHA1: 6501d1d201160520f5aad29d0f9007c17b7d9778 MD5sum: eb090e568264d2f439892bcb98485b8c Description: Neuroimaging in Python NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. nipy-suite is a metapackage depending on the projects developed under NiPy project umbrella, such as - nibabel: bindings to various neuroimaging data formats - nipy: analysis of structural and functional neuroimaging data - nitime: timeseries analysis - dipy: analysis of MR diffusion imaging data - nipype: pipelines and worfklows Package: nipy-suite-doc Source: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 32 Depends: python-nibabel-doc (>= 1.0.0), python-nipy-doc (>= 0.1.2+20110114), python-dipy-doc (>= 0.5.0), python-nipype-doc (>= 0.3.3), python-nitime-doc (>= 0.2) Suggests: python-mvpa-doc Homepage: http://www.nipy.org Priority: extra Section: doc Filename: pool/main/n/nipy-suite/nipy-suite-doc_0.1.0-2_all.deb Size: 2250 SHA256: 54985bd9d6eaa352608b357f2deeb066bd2ac12d3c2e463082f5d9178701bbad SHA1: 5d2f5e94ff6b7ff737fe966f4a2e5ff67df93cca MD5sum: 37d2f8b6b6d203edf208afb0cdb56fa3 Description: Neuroimaging in Python -- documentation NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. . nipy-suite-doc is a metapackage depending on the documentation packages for NiPy projects. Package: openelectrophy Version: 0.0.svn143-1~squeeze.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 92 Depends: python, python-pyssdh (= 0.0.svn143-1~squeeze.nd1) Homepage: http://neuralensemble.org/trac/OpenElectrophy Priority: extra Section: science Filename: pool/main/o/openelectrophy/openelectrophy_0.0.svn143-1~squeeze.nd1_all.deb Size: 34368 SHA256: d3c29b416792bf1d8ca68eb2af7da3b0d60a8f0d836fa9d1d3b83cdd9329b878 SHA1: 1f8d2aca09d37c8e5efb01093a0e10909a862e38 MD5sum: 78bfb172b4686b3985ab9ee42929d028 Description: data analysis framework for intra- and extra-cellular recordings This software aims to simplify data and analysis sharing for intra- and extra-cellular recordings. It supports time frequency plots, spike detection, spike rate calculation, and analysis of phase locked signals. . Data handling and storage utilizes a MySQL database, allowing to handle large amounts of data easily and efficiently. Therefore, a MySQL server running locally or on a remote machine is required. . This package provides the OpenElectrophy GUI. Package: opensesame Version: 0.25-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5000 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2 Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.25-1~nd60+1_all.deb Size: 2848320 SHA256: 58948c84e9228a9e22dea24ca4212d773a382434699c4992ceeef649a11c894b SHA1: 2836bdf7bfba16409068eacc423ac1d676be50fd MD5sum: 5cfba2e2be4dbc12cbaecf65ea9b4ea8 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Python-Version: 2.6 Package: packaging-tutorial Version: 0.5~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1155 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.5~nd+1_all.deb Size: 1111034 SHA256: 3410f99232ee6a8cff11c2d97b4cd50f56d4ae5d71f5dadaa077d92457842996 SHA1: 8756d44b1a608c8c0e29fde5813d6146e67c5026 MD5sum: 7d653f7b7bc96d627e73720627567851 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.73.06.dfsg-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5276 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, libavbin0, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.73.06.dfsg-1~nd60+1_all.deb Size: 2688738 SHA256: 31947bccc99b25a456bfff5ed9ae396755c650fe1cc12117f13921cf8f5a4fb9 SHA1: d51bf907617cc2dd18c7cbf44d94bd9d4f9079c7 MD5sum: 7828c753f3c1c6ddafce541019eaf83d 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: 2.5, 2.6 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2579.dfsg1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 53608 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.9+svn2579.dfsg1-1~nd60+1_all.deb Size: 19434134 SHA256: 0e6260df6b58c18ccff4b4b50fd9c2e7b63d5d6c66ca3ccc1f011d8fd71ebead SHA1: 3b70012ff199b809a2d1573ae55e4fd30e11547e MD5sum: 5c0c4603b99e0ccf6dcac768c45929a6 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: 1.3.0-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1692 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.3.0-2~nd60+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_1.3.0-2~nd60+1_all.deb Size: 314048 SHA256: 827f0572c12c8e2dc30b7df6ca851b9c990d2b820d7740a52033375a908c7b26 SHA1: 278e2c840a9389f89caa142c8e0ef9ed72dc4744 MD5sum: aa070370e087f8b95e590aeaf7aa7e9a 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: 1.3.0-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5320 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_1.3.0-2~nd60+1_all.deb Size: 1651040 SHA256: 2f3be42514fca02c8294450f861152fde3098e9ad06d6fdd5f21793e9e5deb11 SHA1: 8b699779fb48949a5b413547a23b0b17e29eae19 MD5sum: b327e4084febad0f56b621c98bd4349a 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-cfflib Source: cfflib Version: 2.0.5-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 768 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-numpy, python-networkx (>= 1.4), python-nibabel (>= 1.1.0) Recommends: python-nose, python-sphinx, python-tables, python-h5py Provides: python2.6-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd60+1_all.deb Size: 217692 SHA256: 89c8c15b49c321ab86c69d97c6eb00eb731b2bd699c40e38dc56f8eae505412c SHA1: 6bf2302a69863a6985783df190603edfb88b7417 MD5sum: 68cfd02459ffb3eca1787a8a7bb959d2 Description: Multi-modal connectome and metadata management and integration The Connectome File Format Library (cfflib) is a Python module for multi-modal neuroimaging connectome data and metadata management and integration. . It enables single subject and multi-subject data integration for a variety of modalities, such as networks, surfaces, volumes, fiber tracks, timeseries, scripts, arbitrary data objects such as homogeneous arrays or CSV/JSON files. It relies on existing Python modules and the standard library for basic data I/O, and adds a layer of metadata annotation as tags or with structured properties to individual data objects. Package: python-dicom Source: pydicom Version: 0.9.6-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1896 Depends: neurodebian-popularity-contest, python2.6 | python2.5, python (>= 2.6.6-3+squeeze3~), python (<< 2.7) 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_0.9.6-1~nd60+1_all.deb Size: 390312 SHA256: 919a87d2f220b16a48c2c41da4e673550ff1fe4867927495d22514593cfdb8d7 SHA1: a2611209ad87d0b8b560abb8fde0a3a0ad3551e1 MD5sum: 9088b841e73b0cbc29af4edbb06511bb 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: 0.5.0-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2068 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.5.0-2~nd60+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.5-dipy, python2.6-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.5.0-2~nd60+1_all.deb Size: 1457522 SHA256: 46325edad837ac6a4a9c49e857d85e8ea3f97c5287295c64091e041657603b0b SHA1: 9ab04398085bd6386ecfc29605de87b0374e87cf MD5sum: 59a1fcdd7103a9abd9a4e14d0e4bafa3 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: 2.5, 2.6 Package: python-dipy-doc Source: dipy Version: 0.5.0-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3224 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_0.5.0-2~nd60+1_all.deb Size: 1943674 SHA256: 7354aa71350ecee3334dbbc688bf7dde6f6cab57d3cdca5a8effc3b8c3922a3a SHA1: 5bf007b3a1694b96a555d25a70160a708fa1aa16 MD5sum: 804d946e5d628819438b23760007c57e 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: 0.6.4-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 252 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.6.4-1~nd60+1_all.deb Size: 51606 SHA256: 3801bc482f212d4c4b75d154358ba41fdf5a54a98a89235a7606e79595eb21c6 SHA1: ff0f105359081a82242a4289cd3cca058449a5a1 MD5sum: 8e7a42e4a5f7069730a81c69072aeab6 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python2.6 | python2.5, python (>= 2.6.6-3+squeeze3~), python (<< 2.7), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd60+1_all.deb Size: 7430 SHA256: 4add14c7519849f9f98eba7976f7389e162a5cd8d03e6bc778c64a42536fa109 SHA1: 89e6fc5f690f0797b4cee2e135c7ee0a21ea8187 MD5sum: ef8002ce29ca25685369e74069640fc1 Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.2+git78-g7db3c50-3~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1912 Depends: neurodebian-popularity-contest, python2.6 | python2.5, python (>= 2.6.6-3+squeeze3~), python (<< 2.7), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.2+git78-g7db3c50-3~nd60+1_all.deb Size: 482284 SHA256: 47c5ade52011cbb8cedcd5736a2615fc57ed04f8ec512523d9ad2a4fadaf0ea2 SHA1: 596af6dfac1c79feeb58ae4e55d038f378009cd0 MD5sum: 2ccd0ef7a1e456cc32ba1809f7f69565 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-mlpy Source: mlpy Version: 2.2.0~dfsg1-1~squeeze.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 428 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mlpy-lib (>= 2.2.0~dfsg1-1~squeeze.nd1) Suggests: python-mvpa Provides: python2.5-mlpy, python2.6-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy_2.2.0~dfsg1-1~squeeze.nd1_all.deb Size: 58266 SHA256: 77f4b8e2129db61e00feaad3c1460a923975820c91e625dc4fff605039f14c7a SHA1: 878fa1b9c71726e276b82d462006a5a90c127ea6 MD5sum: 69d292f9dfb2f666d6a3542ddbe60dd3 Description: high-performance Python package for predictive modeling mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, *RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators. Python-Version: 2.5, 2.6 Package: python-mlpy-doc Source: mlpy Version: 2.2.0~dfsg1-1~squeeze.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1136 Depends: libjs-jquery Suggests: python-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: doc Filename: pool/main/m/mlpy/python-mlpy-doc_2.2.0~dfsg1-1~squeeze.nd1_all.deb Size: 480866 SHA256: a1a158d0318129c2b6ac767cf0385b266a45aeaa6a06a45fc5bf61d6a77ff9b5 SHA1: 0de7a2884bfd8de60215558a742d138d0d35f167 MD5sum: 676b76390bb77f41f7a1ee949b11e212 Description: documention and examples for mlpy mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . This package provides user documentation for mlpy in various formats (HTML, PDF). Package: python-mpi4py-doc Source: mpi4py Version: 1.2.2-1~pre1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 272 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.2.2-1~pre1~nd60+1_all.deb Size: 54806 SHA256: 1b60db1309827d5c6ca4de2674c4133a7fe851d1fcc86d6a5d13043ed75c76a8 SHA1: cedce687642d97f89416079719540eedd3c926a1 MD5sum: 8365de41874844b3114055398c97d734 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4104 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (<< 1:1.5), python-numpy (>= 1:1.4.1), python-support (>= 0.90.0), python2.6, python-mvpa-lib (>= 0.4.8-1~nd60+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: python2.5-mvpa, python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd60+1_all.deb Size: 2205030 SHA256: f198dd1180f10001be495143f5370afdda65f56a1af0aec6d5000cb381b79589 SHA1: 94d71c82ffa6ee99040d6f6e2485567e477c9c45 MD5sum: 891f70fa8ff33dec72eeb01a687191c8 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: 2.5, 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41276 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~nd60+1_all.deb Size: 8760270 SHA256: ba87abd25596d8843f515761d31fbf29a21cbbbe85a1fb00c1d7c237c273f0ea SHA1: ae46a082ba247faab1cf1ce73de7d97088d0560a MD5sum: 3ede16fa2a8698ddd6e116567f4d2862 Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa2 Source: pymvpa2 Version: 2.1.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4800 Depends: neurodebian-popularity-contest, python (>= 2.4), python-numpy (>= 1:1.4.1), python-numpy (<< 1:1.5), python-support (>= 0.90.0), python-mvpa2-lib (>= 2.1.0-1~nd60+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: python2.5-mvpa2, python2.6-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.1.0-1~nd60+1_all.deb Size: 2354504 SHA256: 95a5f20a374446cd5395af7a58a4b9c4a78bed3ee7926032054c6068fba15a83 SHA1: 6bdb8b86edbcfdb4ac00019726956cec0ef0b566 MD5sum: 9667a2651b01c4bc39ea0692030e9df5 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: 2.5, 2.6 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.1.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25420 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.1.0-1~nd60+1_all.deb Size: 5033504 SHA256: 41506e03eeb32976b03e597f0ce7bfd10a0df0310dfb2df605c228d3d0a63d66 SHA1: 0445e4152a56bc9c97012e94c9713e9d256d7d3b MD5sum: 0481a37dab91d25c1dbe4437fa466746 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.) as well as example scripts. Package: python-networkx Version: 1.4-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2672 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-numpy, python-scipy, python-pygraphviz | python-pydot, python-pkg-resources, python-matplotlib, python-yaml Homepage: http://networkx.lanl.gov/ Priority: optional Section: python Filename: pool/main/p/python-networkx/python-networkx_1.4-2~nd60+1_all.deb Size: 647278 SHA256: ad2839debf74b059def0e377f52e5b3fad23613603d2f69c61a6a7f59bfbd6b7 SHA1: ac9dd5bce62e8f0e1460bf9cff1b4655278cb7fb MD5sum: 88fcc837ad2b6e0c5bcf56df2802b09d Description: tool to create, manipulate and study complex networks NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. Package: python-networkx-doc Source: python-networkx Version: 1.4-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15788 Depends: neurodebian-popularity-contest Homepage: http://networkx.lanl.gov/ Priority: optional Section: doc Filename: pool/main/p/python-networkx/python-networkx-doc_1.4-2~nd60+1_all.deb Size: 6169452 SHA256: c55591f29b87d1772fdf11a511fee43512b88d27dbdb99b0083c2d131b8ffdd6 SHA1: 15e7a5d65dfdb7ebc585a56a55441a4240644b2b MD5sum: feabea6baf7cf83997120652132961f9 Description: tool to create, manipulate and study complex networks - documentation NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. . This package contains documentation for NetworkX. Package: python-nibabel Source: nibabel Version: 1.2.2-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4452 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.5-nibabel, python2.6-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.2.2-1~nd60+1_all.deb Size: 1822018 SHA256: 2d9ce4694a3388e1b5bc4be0be021d52128982a0f009bb154d1a0a0515136fd6 SHA1: f377050b2b946f5322e07bb5b73d0712be911499 MD5sum: ad83bd52ff37aabad87f3b10d28dd2c3 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: 2.5, 2.6 Package: python-nibabel-doc Source: nibabel Version: 1.2.2-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2844 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.2.2-1~nd60+1_all.deb Size: 420496 SHA256: d3bd518d0e8d5f6ae085ddbf7dde6ed306c70c5c91006870c55eadc11929c5b5 SHA1: e3682e0bc196c6ea7e6df892885a6fb19c77eda9 MD5sum: 6a575f3e14347d4dc3cf88a22bfad439 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-nibabel-snapshot Source: nibabel-snapshot Version: 1.0.0.dev+137+gf1c6-1~squeeze.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 964 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, libjs-jquery, python-scipy Conflicts: python-nibabel Provides: python2.5-nibabel-snapshot, python2.6-nibabel-snapshot Homepage: http://nipy.sourceforge.net/nibabel Priority: optional Section: python Filename: pool/main/n/nibabel-snapshot/python-nibabel-snapshot_1.0.0.dev+137+gf1c6-1~squeeze.nd1_all.deb Size: 469788 SHA256: 88f8f2603bab6606985a137433460486b70e5765b08eba1ca81b8dccd3cfe96f SHA1: 12bd934e7cec2d24b9aec58fd66b592b9b4be485 MD5sum: feea254498444cc7f9827456091e83dc Description: Python bindings to various neuroimaging data formats Currently supported formats are: . * ANALYZE (including SPM2 and SPM99 variants) * MINC * NIfTI * PAR/REC . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.5, 2.6 Package: python-nipy Source: nipy Version: 0.2.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3764 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy (<< 1:1.5), python-numpy (>= 1:1.4.1), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.2.0-1~nd60+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.5-nipy, python2.6-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.2.0-1~nd60+1_all.deb Size: 763384 SHA256: 59777155bb656b12cfd716d877a79f4dbacff6bc7c1fd4d835887d96908d8e62 SHA1: bf5ae0b2fc386f52fdec77ba9bfdfc8a6a11f5fd MD5sum: bf72007a1ceafecd4359767f1c47964d 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: 2.5, 2.6 Package: python-nipy-doc Source: nipy Version: 0.2.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9548 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.2.0-1~nd60+1_all.deb Size: 2417966 SHA256: 42ea11bfd07e0e9841495c2b5e8982ef4bd7b048bb149fc611a1210d6b7652ce SHA1: a0c60a64d2cea186308efbb5fe69ffc830e99309 MD5sum: 33bdc7197bd0d888bc5ec29b086b080b Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.6.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3064 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.6.0-1~nd60+1_all.deb Size: 521738 SHA256: 6fb149f42819bc6b21d34e4042ceea52b1038cc54ade990f6e0fe878638618b1 SHA1: b74b89073ff1b76820717b78ded5ae0ee58b2b43 MD5sum: 9799bdbfefa654df09c4ea5397dd7ab8 Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.6.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14244 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.6.0-1~nd60+1_all.deb Size: 6319440 SHA256: 71ae3ae19f2af0c9b3dfa396512d5aee6463eca9c6fb633f60e60404c10a797d SHA1: 384211cb83f9f3fcac7522db822a314543f0b37a MD5sum: f27e5924c2fba4ece026fe41528dfcb5 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9444 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.4-2~nd60+1_all.deb Size: 3908874 SHA256: 03bb69e85b0c60f62d7c56de8f7cfa9e05fc93746205fe87df6ef05a2aef914d SHA1: 599b2f49a8f7c78bdcbed469dbd4e81ee12cca1c MD5sum: 5ba3ac0c3d3a250dfdeaae5229b9f8fc 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7124 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~nd60+1_all.deb Size: 5300740 SHA256: ef5d041daa508131e2dc3f7fd82ffdf1311a430e6921ee392f1fb850ed8589f8 SHA1: 8c01d570fd9eac4b59af7392e487017fcbf68aca MD5sum: 16390cff8be229413da22076a622ea8f 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1612 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.5-openopt, python2.6-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd60+1_all.deb Size: 245078 SHA256: 99d7232ca419c672cc667708687c3347fa7683e431d22124840bd2d6f70c02ae SHA1: e7209294307c6d27e9b1983f7362a6afe45b934b MD5sum: bedd41cecd50b21a5d02c1db1d0a2767 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: 2.5, 2.6 Package: python-openpyxl Source: openpyxl Version: 1.5.8-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 504 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.5.8-1~nd60+1_all.deb Size: 71602 SHA256: 481915a2f54033c9feedf8042c462fa0379fb75eff7c2bcce8ba75ab42ab4ab7 SHA1: dc14aca2cb5665cdc1f92509700c44a90c2633b4 MD5sum: 6f387b7bd0ca97384191248ff08dc707 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pandas Source: pandas Version: 0.7.3-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2220 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-dateutil, python-pandas-lib (>= 0.7.3-1~nd60+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.5-pandas, python2.6-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.7.3-1~nd60+1_all.deb Size: 460866 SHA256: ee9fa4862c1988c184069c02ff59b90d4c4751c1cd0d9185eed4030842cc7072 SHA1: a49274e19f474958baf2e0fe8e39438f169fb6f1 MD5sum: 307fb7d696545ea68a70c3ef2f5836c6 Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.5-pyentropy, python2.6-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd60+1_all.deb Size: 21332 SHA256: 6175773981eb53152667c5a08a77b4e4a419bbc59537f6d2b307d56b7478e881 SHA1: 332090c698b49a4bde76a7c084042976322f6fde MD5sum: ec77541c844fd6130c1abd2435b821e1 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: 2.5, 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~squeeze.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 852 Depends: python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0-3~squeeze.nd1_all.deb Size: 817820 SHA256: 575a264fe983d8b7d0ad9eaac6baae7c46308bfea1a454dd466636f7cd9b60da SHA1: 219e559bf4ac39efbc3f0e375cf3ea8849d1d224 MD5sum: b3492c37881b41822afe7760f1b3cc5a 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-pyglet Source: pyglet Version: 1.1.4.dfsg-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4356 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-ctypes | python (>= 2.5), libgtk2.0-0, libgl1 | libgl1-mesa-swx11, libglu1 | libglu1-mesa Recommends: libasound2 | libopenal1 Provides: python2.5-pyglet, python2.6-pyglet Homepage: http://www.pyglet.org Priority: optional Section: python Filename: pool/main/p/pyglet/python-pyglet_1.1.4.dfsg-1~nd60+1_all.deb Size: 972196 SHA256: 91b6b5b43bba43c419bc93e875ebba6ac09733899d7d34e944a5df43c3a33a6c SHA1: d9cb126e2761a5bd4b56f73542eac4dadea3f185 MD5sum: e3b5a0fd56d17deacf83460ebcea6737 Description: cross-platform windowing and multimedia library This library provides an object-oriented programming interface for developing games and other visually-rich applications with Python. pyglet has virtually no external dependencies. For most applications and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. It also handles multiple windows and fully aware of multi-monitor setups. . pyglet might be seen as an alternative to PyGame. Package: python-pynn Source: pynn Version: 0.7.4-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1024 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.4-1~nd60+1_all.deb Size: 191936 SHA256: 9267d487a384e4ebcfeb2d0c909f194c244f9af875fab356ed67b7a087768ff0 SHA1: a168546d3a67361f760619bcea0d4a7d1b5c0517 MD5sum: 6be6998bcef2a58b3e209c093b109104 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-pyoptical Source: pyoptical Version: 0.2-1~squeeze.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72 Depends: python-serial Enhances: psychopy, python-pyepl Homepage: http://github.com/esc/pyoptical Priority: extra Section: python Filename: pool/main/p/pyoptical/python-pyoptical_0.2-1~squeeze.nd1_all.deb Size: 6956 SHA256: 66717fa53f6d283a3a697f969f32bc1c15f1467bbc26bb09ffceba7beb871644 SHA1: 3201dafeb370ade84db53fbe0ce85c1a0e57455c MD5sum: cf68976930753cdd2fde4b74529ba1b6 Description: python interface to the CRS 'OptiCAL' photometer The 'OptiCAL' is a photometer that is produced by Cambridge Research Systems (CRS). This device is a standard tool for gamma-calibration of display devices in vision research. This package provides a free-software replacement for the Windows-software distributed by the manufacturer that allows querying an OptiCAL via a serial connection. pyoptical can be used as a library for third-party applications or as a standalone command line tool. Python-Version: 2.5, 2.6 Package: python-pyssdh Source: openelectrophy Version: 0.0.svn143-1~squeeze.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 792 Depends: python-support (>= 0.90.0), python-numpy, python-scipy, python-qt4, python-mysqldb, python-matplotlib Recommends: g++ | c++-compiler, python-mdp Suggests: mysql-server Provides: python2.4-pyssdh, python2.5-pyssdh Homepage: http://neuralensemble.org/trac/OpenElectrophy Priority: extra Section: python Filename: pool/main/o/openelectrophy/python-pyssdh_0.0.svn143-1~squeeze.nd1_all.deb Size: 119516 SHA256: 1adaffa1132d6581ae599f8781f656a482fb586ecdaa789ab235068043a7f85f SHA1: bd3b2114258a93dbd1108eaea341f8541ff74a47 MD5sum: 1f942f44319f70c9cc3afcaac2e70796 Description: data analysis framework for intra- and extra-cellular recordings This software aims to simplify data and analysis sharing for intra- and extra-cellular recordings. It supports time frequency plots, spike detection, spike rate calculation, and analysis of phase locked signals. . Data handling and storage utilizes a MySQL database, allowing to handle large amounts of data easily and efficiently. Therefore, a MySQL server running locally or on a remote machine is required. . This package provides the OpenElectrophy Python module. Python-Version: 2.4, 2.5 Package: python-pyxid Source: pyxid Version: 1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Homepage: https://github.com/cedrus-opensource/pyxid Priority: optional Section: python Filename: pool/main/p/pyxid/python-pyxid_1.0-1~nd+1_all.deb Size: 11020 SHA256: 1031c0d69dd73cb38f3e0b826193211706a94bfd04da4287288418b257e54249 SHA1: 0f0d0524354e5d07eb89efcb11779d9acd9d57e2 MD5sum: 1f2a9bc07952b1f5c6b65fc5c092f75c Description: interface for Cedrus XID and StimTracker devices pyxid is a Python library for interfacing with Cedrus XID (eXperiment Interface Device) and StimTracker devices. XID devices are used in software such as SuperLab, Presentation, and ePrime for receiving input as part of stimulus/response testing experiments. . pyxid handles all of the low level device handling for XID devices in Python projects. Package: python-scikits-learn Source: scikit-learn Version: 0.11.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, python-sklearn, python (>= 2.6), python-support (>= 0.90.0) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.11.0-1~nd60+1_all.deb Size: 22866 SHA256: 26cbdf2c5ac1294d216375d6dfc8e5c93c732a846adc80a8e625add3bce7bb5f SHA1: e59d59df149e851221e634234e47ef7e10c0e60f MD5sum: cc83ec4995551ff443a6d0b0365fa495 Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.4.2-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 120 Depends: neurodebian-popularity-contest, python-statsmodels, python (>= 2.5), python-support (>= 0.90.0) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.4.2-1~nd60+1_all.deb Size: 10268 SHA256: 584640f2cdcd4739396ad91847b24d26b62e22bf4ee83aff86c59ef90288f67b SHA1: 2908e6aa9a99de3da575f40c400dde4c5b2159ee MD5sum: 4d1cc0505c5aaba51dd5dc8b5d6aba29 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-scikits.statsmodels-doc Source: statsmodels Version: 0.3.1-4~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 18676 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits.statsmodels Conflicts: python-scikits-statsmodels-doc Replaces: python-scikits-statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-scikits.statsmodels-doc_0.3.1-4~nd60+1_all.deb Size: 1877926 SHA256: 04f2fe71ec6b26f50da74dc8ebe35e50ba871387fc66c66ef96279b911381521 SHA1: 3e2c0b1039d4281f94b63eb8ae3a213f171e4a78 MD5sum: c78f5a53086cef135dbad5830eeb5159 Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.5-simplegeneric, python2.6-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd60+1_all.deb Size: 9802 SHA256: a1f16f30724b88550716edbaacbaedaea6dbcc88a2a5e22f375896ba31e71c5e SHA1: af5b697130da85854bdb717319c7ae2aa719b9ae MD5sum: 34b6361e577be81e7cc33b0597a0b491 Description: Simple generic functions for Python The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library. Package: python-sklearn Source: scikit-learn Version: 0.11.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3072 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.11.0-1~nd60+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.6-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.11.0-1~nd60+1_all.deb Size: 890950 SHA256: 65201a931b54ff5aac87b7359bf00123a300b69da7592bae89551eb4a465a3ad SHA1: 4da61b6d1d37a57d7a3946222eb8ac5c23d3b0be MD5sum: 455953ca1f65a028d336d38696573dab Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.6 Package: python-sklearn-doc Source: scikit-learn Version: 0.11.0-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29300 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.11.0-1~nd60+1_all.deb Size: 16288058 SHA256: ff02ead3617408822dae431bb1bd2fa6a25264ce50e9a1e8abd04636b248f47c SHA1: bb6d0474f2d3657df9058685deb47ced1612fb00 MD5sum: a9e777266706ba7af68c55703d06cf64 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4188 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-docutils (>= 0.5), python-pygments (>= 0.8), python-jinja2 (>= 2.2), libjs-jquery Recommends: python (>= 2.6) | python-simplejson, python-imaging Suggests: jsmath Homepage: http://sphinx.pocoo.org/ Priority: optional Section: python Filename: pool/main/s/sphinx/python-sphinx_1.0.7-2~nd60+1_all.deb Size: 1260210 SHA256: 5a134abec0131a6dcc56b85cd9089230b68374cc7e4896d8806d7e6e2e9ee9a7 SHA1: 21654aba4316d6b6799f864a41f925c64adf8725 MD5sum: 3968ce5358f08a65453ba21236af6630 Description: tool for producing documentation for Python projects Sphinx is a tool for producing documentation for Python projects, using reStructuredText as markup language. . Sphinx features: * HTML, CHM, LaTeX output, * Cross-referencing source code, * Automatic indices, * Code highlighting, using Pygments, * Extensibility. Existing extensions: - automatic testing of code snippets, - including doctrings from Python modules. Package: python-spyderlib Source: spyder Version: 2.1.9-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4356 Depends: neurodebian-popularity-contest, python2.6, python (>= 2.6.6-3+squeeze3~), python-qt4 Recommends: ipython, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-rope, python-numpy, python-scipy, python-sphinx (>= 0.6.0) Breaks: python (>= 2.7), spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.6-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.1.9-1~nd60+1_all.deb Size: 1651386 SHA256: 48ed4ca4c5d1220d0421caa43c690bc74d9aa9b9b35a757b8a6bd27891095e3d SHA1: 81c1652a63066085e2fc437e224e62016aabae7c MD5sum: a8d585f3f77150f8525949ff3a3ceb8b Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-statsmodels Source: statsmodels Version: 0.4.2-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13468 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.2-1~nd60+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: python2.5-statsmodels, python2.6-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.4.2-1~nd60+1_all.deb Size: 3088722 SHA256: 2ef04a8c3b25f5f53d2b4565225c266c1ffd56cd603870642d1200f237b322b1 SHA1: 7adb86840f38d4012a854a18baba7d99f400d5e8 MD5sum: 7f239aa8214f3c27853703ceb1882fb3 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24480 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~nd60+1_all.deb Size: 4018734 SHA256: 1b4131d7dd9a9166d666a66a5440d88fd0bf0f43da13a3410570212767b4d08b SHA1: aae094e6e65be8a31faa8a40f3e042c7a9551dea MD5sum: 9b877a84be82b5a435144ba95a80a2c2 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~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 156 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd60+1_all.deb Size: 28734 SHA256: 145a0c1b54cbaf35a29e42593256969b33e63bf3ecf76b7fbf094e691a9cd89b SHA1: 1355d1c6399e26dff01191cf4cf0d07a401a06e0 MD5sum: f82cecb6d7af2eaae4f1ac89cfc3918b 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 Package: python-sympy Source: sympy Version: 0.6.7-1.1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9268 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Recommends: python-imaging, python-ctypes, ipython Homepage: http://code.google.com/p/sympy/ Priority: optional Section: python Filename: pool/main/s/sympy/python-sympy_0.6.7-1.1~nd60+1_all.deb Size: 1696348 SHA256: 90437808b931d5eb683327ab48a3ca8e81092be6f14d7f9cdf3f1fd8c8e6381d SHA1: 8ff9042d8752320997021155b1d7ee3620d11545 MD5sum: 38368c397ca1f942608ee78c4d6f1a8f Description: Computer Algebra System (CAS) in Python SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. Package: python-tz Version: 2011h-0.1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 180 Depends: neurodebian-popularity-contest, tzdata, python, python-central (>= 0.6.11) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2011h-0.1~nd60+1_all.deb Size: 46922 SHA256: 358ffca351d867546f34bb4e9393d6944e63e7a817f3a1eb704396a28faa3bfc SHA1: 1f3c649c4ec7910443ecc3b70d458f04a118e77b MD5sum: 4eabe87106517e2815abcd9c5f491784 Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Python-Version: all Package: qlandkarte Source: qlandkartegt Version: 0.16.0-1~squeeze.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 32 Depends: qlandkartegt Homepage: http://www.qlandkarte.org Priority: optional Section: x11 Filename: pool/main/q/qlandkartegt/qlandkarte_0.16.0-1~squeeze.nd1_all.deb Size: 2600 SHA256: 971cfe8965e2ac770ab91d1ff374cd8a75c9c59d21a4a3a6c2fec65f0aa36f27 SHA1: 94b85cfadc8414252933de2d0bab789f82ea1161 MD5sum: 461d6da351ea7fcd3dbffa8c5a5bfcf3 Description: Transitional package for QLandkarteGT This is a transitional package for the QLandkarte to QLandkarteGT upgrade, and can be safely removed after the installation is complete. Package: slicer-data Source: slicer Version: 3.4.0~svn10438-3~squeeze.nd1 Architecture: all Maintainer: Debian Science Team Installed-Size: 75656 Depends: tk8.5 | wish Homepage: http://www.slicer.org/ Priority: optional Section: doc Filename: pool/main/s/slicer/slicer-data_3.4.0~svn10438-3~squeeze.nd1_all.deb Size: 45850452 SHA256: c5a750d8b5ae619e7676d13bc9f8975e081771cfe9b6d534b000b54968903d3f SHA1: 34f83bdb09471100da1c6ea84b67a6064bf20708 MD5sum: 7470a7eb5cb992fb85799cd88960ff69 Description: software package for visualization and image analysis - share Slicer is an application for computer scientists and clinical researchers. The platform provides functionality for segmentation, registration and three-dimensional visualization of multi-modal image data, as well as advanced image analysis algorithms for diffusion tensor imaging, functional magnetic resonance imaging and image-guided therapy. Standard image file formats are supported, and the application integrates interface capabilities to biomedical research software and image informatics frameworks. . 3D Slicer data files. Package: spm8-common Source: spm8 Version: 8.4667~dfsg.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22352 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4667~dfsg.1-1~nd60+1_all.deb Size: 10573690 SHA256: 8aa9f613db4d596b62c16b09218f3b0c7b0d598d936da22209e971b3369e5c89 SHA1: fd12e9e25ed9aa391403e70d2666fe40a0fe6bc6 MD5sum: 00d2e40ee0e82c296451f0a7940e6aa0 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.4667~dfsg.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73084 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4667~dfsg.1-1~nd60+1_all.deb Size: 52167706 SHA256: 162599ca35a4e161c1a068d6dc58c5e3047e4caaa3a915b590edbdb8fe39af08 SHA1: a6159ab4fb7476743b2f9379b5542f04e0c9b75a MD5sum: 5b9abe3d26aa01b34f036ba69ebd591c Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.4667~dfsg.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9380 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4667~dfsg.1-1~nd60+1_all.deb Size: 8648912 SHA256: d5745267f4bd85af983f231f84633d2d7b9235150fe4ec2b02dbb9a3a366ab90 SHA1: 459809faf98947b5f7e8e3df7d7f9825913c134a MD5sum: b503c563c050f4cbab6dc052b709ec54 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: spyder Version: 2.1.9-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, python, python-spyderlib (= 2.1.9-1~nd60+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.1.9-1~nd60+1_all.deb Size: 16812 SHA256: 48389bc87d994efc4e0b3c318fa8d42271e51887381a9d6db7b3ac8b981fd3fe SHA1: ca1ab13507cd8f2db8b5ec51b8657686115568e9 MD5sum: f581250e21943f323f2286977f8fd610 Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: stabilitycalc Version: 0.1-1~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd60+1_all.deb Size: 28608 SHA256: 997379d03b4381e98ba743db58b918585f79a077ba7dbb726e745841a0ac402e SHA1: 1efb3b900d33eec54f3893e4affe87eda15bd8d2 MD5sum: 66d13f8fbd8d79e87fa99a0e8cab8cd0 Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.5, 2.6 Package: svgtune Version: 0.1.0-2 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 64 Depends: python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-2_all.deb Size: 6680 SHA256: 69b4df1e0b4c247673265c7f5bb2b2ffe2209d783617bf7f6eadce86633f80e1 SHA1: bec339e4453c35a05a616deef6769a6f2ad2d00d MD5sum: ef6797498477a73f930ad9bc0db3ba73 Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png). Package: ubuntu-keyring Version: 2010.+09.30~nd60+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 32 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd60+1_all.deb Size: 11798 SHA256: 6cbcf7d81718e041431125e45215b746615d2012dc64a2a6c9d2a30e4826fed3 SHA1: a7ba0e713d1052a7aa26930c48dc4aaf6e97bbd1 MD5sum: cb5b41c6b935192df8432bce736f15b6 Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that.