Package: arno-iptables-firewall Version: 1.9.2.k-3~karmic.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~karmic.nd1_all.deb Size: 132478 SHA256: baf52294b6f11e2d1d499799363b0c7428eb6c5519511130d5188a452356ef2e SHA1: 6e354743a73c7f42e4461003e1bf7f321ee2df79 MD5sum: c3441a52075f488db2dbb4d448f7deaf 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~nd09.10+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~nd09.10+1_all.deb Size: 73018 SHA256: d7ddfcad626dbb0f16b190d704a02cb4b73e51256fff26e999fd56dbd9eeeabf SHA1: 3b98c0a9b16ae6df62dbeb5ec7e1c11a60c211d1 MD5sum: f997e6eda1ebf85f67e25a05fffe8f2a 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: biosig-tools Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 652 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_0.94.2+svn2552-1~pre1~nd09.10+1_i386.deb Size: 251520 SHA256: af3cbd5cbf233c0785f56f4ea543021fe1c0cbe5715475c33354fcca0b3eae51 SHA1: 24bd89c44c8b3f9896240a7d2539c2cc28f4f347 MD5sum: 17a3cb9a30a30fc64481b9a41be525e6 Description: format conversion tools for biomedical data formats Based on BioSig library, this package provides command line tools, such as * save2gdf: converter between different file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF, BDF, CWFB. save2gdf can be also used to upload or retrieve data from a bscs server. TODO... Extend? ship client/server? Package: caret Version: 5.6.2~dfsg.1-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 18680 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgomp1 (>= 4.2.1), libminc2-1, libqt4-assistant (>= 4.5.1), libqt4-network (>= 4.5.1), libqt4-opengl (>= 4.5.1), libqt4-xml (>= 4.5.1), libqtcore4 (>= 4.5.1), libqtgui4 (>= 4.5.1), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvtk5.2, zlib1g (>= 1:1.2.3.3.dfsg) Recommends: qt-assistant-compat Suggests: caret-data (>= 5.6.2~dfsg.1~) Homepage: http://brainvis.wustl.edu/wiki/index.php/Caret:About Priority: optional Section: science Filename: pool/main/c/caret/caret_5.6.2~dfsg.1-1~nd09.10+1_i386.deb Size: 7283928 SHA256: 5b2001c7f0aa5f95be979d30e0ae262d4a3fa105ed5b90ce06b048cfbaff0988 SHA1: 275f354578cf15ff2a2f88eb31a4f61d5138ff4e MD5sum: f9b35c68873bf94e5f36b66a46cad332 Description: Computerized Anatomical Reconstruction and Editing Toolkit This software allows for creating, viewing and manipulating surface reconstructions of the cerebral and cerebellar cortex, viewing volumes and for displaying experimental data on the surfaces and volumes. While Caret is primarily a GUI application with 'caret_command' there is also a versatile command line tool, that allows access to a substantial proportion of Caret's functionality. . Caret can download and use stereotaxic atlases (human, monkey, mouse and rat) from an open online database. . Some functionality of Caret is only available when additional data files, provided by the caret-data package, are available. This includes: . - Map volumes to surface via PALS atlas - Multi-resolution morphing - Projection of foci via PALS atlas - Surface-based registration - Surface flattening . Currently the caret-data package is only available from the NeuroDebian repository. Please see http://neuro.debian.net for more imformation. Package: classads Version: 1.0.9-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 120 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libclassad0 (= 1.0.9-2~nd09.10+1), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: misc Filename: pool/main/c/classads/classads_1.0.9-2~nd09.10+1_i386.deb Size: 35840 SHA256: bf6b8e2483c1623b707f83d6985195d9e1dff9abd96da0a4bc54969a66cb95aa SHA1: 59d4a2dbc3b90401ba3fcbd77a60c2f73ae91d5f MD5sum: 4aaaef615903c64918a53e6ee4423078 Description: Condor's classad utilities A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides command line tools to manipulate, test and evaluate classads. Package: coop-computing-tools Source: cctools Version: 3.3.0~svn1179-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3404 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libfuse2 (>= 2.6), libglobus-common0, libglobus-gss-assist3, libglobus-gssapi-gsi4, libkrb5-3 (>= 1.6.dfsg.2), libmysqlclient16 (>= 5.1.21-1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Suggests: coop-computing-tools-doc Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: utils Filename: pool/main/c/cctools/coop-computing-tools_3.3.0~svn1179-2~nd09.10+1_i386.deb Size: 1276178 SHA256: 08d076008d2e1b043b77933a2f1b8e039456a232585907c74fe59f05c2ed715d SHA1: bd65f569b685b05522a2d816a70934f78c394c65 MD5sum: 48c79c1fbd85b1f091f47aa2cbc31463 Description: cooperative computing tools This is a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. This includes: . Chirp - A personal filesystem and I/O protocol that allows unprivileged users to share space securely, efficiently, and conveniently. When combined with Parrot, Chirp allows users to create custom wide-area distributed filesystems. Parrot - A transparent user-level virtual filesystem that allows any ordinary program to be attached to a remote storage device such as an FTP server or a Chirp server. Makeflow - A workflow system for parallel and distributed computing that uses a language very similar to Make. Work Queue - A system and API for building master-worker style programs that scale up to thousands of processors. All Pairs - A computational abstraction for running very large Cartesian products. Wavefront - A computational asbtraction for running very large dynamic programming problems. The Fault Tolerant Shell - A high-level programming language that allows users to combine the ease of shell scripting, the power of distributed programming, and the precision of compiled languages. Basically, parallel programming and exception handling for scripts. Package: coop-computing-tools-dev Source: cctools Version: 3.3.0~svn1179-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 952 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: libs Filename: pool/main/c/cctools/coop-computing-tools-dev_3.3.0~svn1179-2~nd09.10+1_i386.deb Size: 217302 SHA256: 4ca832677442f05aaef23f8cd92763708efce3bcde4eea445a4dab2d6ebf3d83 SHA1: f8c3f030f6e467f726f16b25b1991b90b33e2c12 MD5sum: ebf164d719f355423867d2e22e75ae78 Description: libraries and header files 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 static libraries and header files for development. Package: coop-computing-tools-doc Source: cctools Version: 3.3.0~svn1179-2~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2304 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.3.0~svn1179-2~nd09.10+1_all.deb Size: 254028 SHA256: 1e605412ad4d53c166e43b7c0ef9e2efc9a9a7133a4466023f91655967accf5b SHA1: b7ee7a4b5155f51e7d956e6d26b3fe7e05f9909b MD5sum: 80f3c6d0bc09a69fd4c6786900d1f685 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: cython Version: 0.13-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4588 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3.6-6~) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd09.10+1_i386.deb Size: 1111086 SHA256: f27157e33a152428b6f50dc43c2accb12b37e778e42d2d964881ebbddf45be9a SHA1: 639408aaf73a7298b9f68f00cfaa9f43ff7295b8 MD5sum: 3fbefd799515980a1cb1e243f2b1555c Description: C-Extensions for Python Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations. . The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code. . This makes Cython the ideal language for wrapping for external C libraries, and for fast C modules that speed up the execution of Python code. Python-Version: 2.5, 2.6 Package: cython-dbg Source: cython Version: 0.13-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 7628 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), libc6 (>= 2.3.6-6~), cython (= 0.13-1~nd09.10+1) Suggests: gcc Homepage: http://cython.org/ Priority: extra Section: debug Filename: pool/main/c/cython/cython-dbg_0.13-1~nd09.10+1_i386.deb Size: 2892452 SHA256: c993c8bd1c493658fa9ef6074d71e10ea55ebe79d9b8f7ff799b30c54dddba54 SHA1: 34658d55a032778abb73fd3d45ac9614f7343af7 MD5sum: e063489f961af02a493eb213a68e2d62 Description: C-Extensions for Python (Debug Build of Cython) This package contains Cython libraries built against versions of Python configured with --pydebug. Python-Version: 2.5, 2.6 Package: dh-autoreconf Version: 2~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, perl, debhelper, autoconf, automake | automaken, libtool Recommends: autopoint Enhances: cdbs, debhelper Priority: optional Section: devel Filename: pool/main/d/dh-autoreconf/dh-autoreconf_2~nd09.10+1_all.deb Size: 11732 SHA256: 19f4f105424770af556411d8e40dbc86dc466dcc668b1094e21f6f8df73724ff SHA1: 46615cfd4ac4d4ca6cd9129f22ecc4754eac7625 MD5sum: c9a6ce162861cac7c16b22874e68e546 Description: debhelper add-on to call autoreconf and clean up after the build dh-autoreconf provides a debhelper sequence addon named 'autoreconf' and two commands, dh_autoreconf and dh_autoreconf_clean. . The dh_autoreconf command creates a list of the files and their checksums, calls autoreconf and then creates a second list for the new files. . The dh_autoreconf_clean command compares these two lists and removes all files which have been added or changed (files may be excluded if needed). . For CDBS users, a rule is provided to call the dh-autoreconf programs at the right time. Package: dicomnifti Version: 2.28.14-2~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 488 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libstdc++6 (>= 4.4.0) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.28.14-2~karmic.nd1_i386.deb Size: 150834 SHA256: 980c4c3ace720ddccd755912bea1c077a78e96beebc75c3a9acaaf036c1810f4 SHA1: 9de8acbea943c1986f4b698c81b773f679e3b157 MD5sum: 090dd33f5be13d1d7b9e77f13e58aaed Description: converts DICOM files into the NIfTI format The dinifti program converts MRI images stored in DICOM format to NIfTI format. The NIfTI format is thought to be the new standard image format for medical imaging and can be used with for example with FSL, AFNI, SPM, Caret or Freesurfer. . dinifti converts single files, but also supports fully automatic batch conversions of complete dicomdirs. Additionally, converted NIfTI files can be properly named, using image series information from the DICOM files. Package: eatmydata Source: libeatmydata Version: 26-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3) Provides: libeatmydata Homepage: https://launchpad.net/libeatmydata Priority: optional Section: utils Filename: pool/main/libe/libeatmydata/eatmydata_26-2~nd09.10+1_i386.deb Size: 7994 SHA256: ed44efa9f26bd1cb50aa3ac292ad6749cd2337d1a7bafb25fd7ed7ba6138bc86 SHA1: 049382b8a8b644fe23dcae7c4c09df3b5fae9f83 MD5sum: 022a2be9309fba7fb938ab0097954ab1 Description: library and utilities designed to disable fsync and friends This package contains a small LD_PRELOAD library (libeatmydata) and a couple of helper utilities designed to transparently disable fsync and friends (like open(O_SYNC)). This has two side-effects: making software that writes data safely to disk a lot quicker and making this software no longer crash safe. . You will find eatmydata useful if particular software calls fsync(), sync() etc. frequently but the data it stores is not that valuable to you and you may afford losing it in case of system crash. Data-to-disk synchronization calls are typically very slow on modern file systems and their extensive usage might slow down software significantly. It does not make sense to accept such a hit in performance if data being manipulated is not very important. . On the other hand, do not use eatmydata when you care about what software stores or it manipulates important components of your system. The library is called libEAT-MY-DATA for a reason. Package: epydoc-doc Source: epydoc Version: 3.0.1-4~karmic.nd1 Architecture: all Maintainer: Kenneth J. Pronovici Installed-Size: 15000 Recommends: iceweasel | www-browser Priority: optional Section: doc Filename: pool/main/e/epydoc/epydoc-doc_3.0.1-4~karmic.nd1_all.deb Size: 1544574 SHA256: 352b6477b048abd019d7aef1f0bda1ecd360cf48cd1bc89cbca010ebe7118666 SHA1: 009a69d67fc361fa30db68eed4b7955d1a8ac0ec MD5sum: 12c6f4a68bfd735d73d401cdfbd8f303 Description: official documentation for the Epydoc package Epydoc is a tool for generating API documentation for Python modules based on their docstrings. A lightweight markup language called epytext can be used to format docstrings and to add information about specific fields, such as parameters and instance variables. Epydoc also understands docstrings written in ReStructuredText, Javadoc, and plaintext. . This package contains the API reference and usage information for Epydoc, all available through the Debian documentation system (dhelp, dwww, doc-central, etc.) in the Devel section. Package: fail2ban Version: 0.8.4+svn20110323-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 596 Depends: neurodebian-popularity-contest, python (>= 2.4), python-central (>= 0.6.11), lsb-base (>= 2.0-7) Recommends: iptables, whois Suggests: python-gamin, mailx Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.4+svn20110323-1~nd09.10+1_all.deb Size: 98010 SHA256: 471505f86faca4d24db8e273ad0174076988f41560abf42df4f663e35025bae0 SHA1: 5cdb1a4e7a4703d0a0cedf6b14bd2f2d17828cb5 MD5sum: 1b75abca4724daadfae38106e054196f 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: fslview Version: 3.1.8+4.1.6-2~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 3864 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.4.0), libvtk5.2, libvtk5.2-qt3 Recommends: fslview-doc Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_3.1.8+4.1.6-2~karmic.nd1_i386.deb Size: 1498060 SHA256: a21d1edb7d8219bc2264b6dd291562b181e0aef4e7b011bd5d3c044f2e253c73 SHA1: aceab9fb1490c40c052c3ee06725d20638018abe MD5sum: d4bc2972997cd57c880b480cb921d75e Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 3.1.8+4.1.6-2~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3216 Depends: 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+4.1.6-2~karmic.nd1_all.deb Size: 2378972 SHA256: 5d4ed542a39f9204afbe6575f07ca17c2d200cecbf569f5518d36f7f852503a9 SHA1: 85ee2eae47a22442e7c39d46f92f510c6d1b6bd7 MD5sum: 665ea4e5860f4aa4e35963956ee99f1a Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gifti-bin Source: gifticlib Version: 1.0.9-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 128 Depends: libc6 (>= 2.3.4), libexpat1 (>= 1.95.8), libgiftiio0, libnifti2, zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: utils Filename: pool/main/g/gifticlib/gifti-bin_1.0.9-1~karmic.nd1_i386.deb Size: 28738 SHA256: bf0291f5696262d0460e052dd26123a473ffd339193c8f091c81aeb70797af34 SHA1: fb397865b70521be68301a3ec185072d8b5c9c89 MD5sum: ca56c7255d45b25cf6342b4cadd76f97 Description: tools shipped with the GIFTI library GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the tools that are shipped with the library (gifti_tool and gifti_test). Package: hdf5-tools Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 1176 Depends: libc6 (>= 2.7), libhdf5-serial-1.8.3 | libhdf5-1.8.3, zlib1g (>= 1:1.1.4) Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: science Filename: pool/main/h/hdf5/hdf5-tools_1.8.3-2.1~karmic.nd1_i386.deb Size: 365984 SHA256: a49e7b254cfb26c032ea50a5cc29873328c8b10b5f8a21a62b7d443b0ac1ec39 SHA1: 176111eae5e2bdf25f6fbfa11284749f8b9b0635 MD5sum: f4b9465a055970c2e630039101d753a3 Description: Hierarchical Data Format 5 (HDF5) - Runtime tools HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime tools for HDF5. Package: itksnap Version: 2.0.0-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 8124 Depends: libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.14, libstdc++6 (>= 4.4.0), libvtk5.2 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.0.0-1~karmic.nd1_i386.deb Size: 3596096 SHA256: e37f08ddc796f2f0ed4dec133c10324b8688037fac662c10e2ee61aa353e2019 SHA1: 73469d7027b400bc092b59f5f6e3d1e97219da53 MD5sum: 9a44fae0e2f6cb76c5112a0b9ee22cbb Description: semi-automatic segmentation of structures in 3D images SNAP provides semi-automatic segmentation of structures in medical images (e.g. magnetic resonance images of the brain) using active contour methods, as well as manual delineation and image navigation. Noteworthy features are: . * Linked cursor for seamless 3D navigation * Manual segmentation in three orthogonal planes at once * Support for many different 3D image formats, including NIfTI * Support for concurrent, linked viewing and segmentation of multiple images * Limited support for color images (e.g., diffusion tensor maps) * 3D cut-plane tool for fast post-processing of segmentation results Package: libbiosig-dev Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1248 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd09.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.2+svn2552-1~pre1~nd09.10+1_i386.deb Size: 369754 SHA256: 870714b8ad06ee72769508c43e7d90b3d141510d2e4c28284a9729ee9fc883bd SHA1: 4cb582aaabe5a7d8f3e5aff73ebaa87794ea20c8 MD5sum: 3c54c1b6bff5e04ead7c2f34427f1439 Description: I/O library for biomedical data - development files BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides header files and static library. Package: libbiosig0 Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 780 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig0_0.94.2+svn2552-1~pre1~nd09.10+1_i386.deb Size: 292006 SHA256: 436966ce205863169a6177b8d7bc29965225537ba4269a4d1833eee789e43961 SHA1: e1da62c1bbe6afc85083916d24bdb87f10f0b3a3 MD5sum: 1896dd586e1866c603cb7fb233f19028 Description: I/O library for biomedical data - dynamic library BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides dynamic library. Package: libbiosig0-dbg Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 640 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd09.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig0-dbg_0.94.2+svn2552-1~pre1~nd09.10+1_i386.deb Size: 179590 SHA256: f5197cc2c0667c7782aff36ca15a4b3c06efa15adc0a044a85b639ed4e9f3053 SHA1: 1e8c30d4a738427a1db76285682c02e8b3aad3d9 MD5sum: 5c6efb6c0c169cd37f5a82d41943d0c5 Description: I/O library for biomedical data - debug symbols BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides debug symbols. Package: libclassad-dev Source: classads Version: 1.0.9-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1688 Depends: neurodebian-popularity-contest, libclassad0 (= 1.0.9-2~nd09.10+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libdevel Filename: pool/main/c/classads/libclassad-dev_1.0.9-2~nd09.10+1_i386.deb Size: 527318 SHA256: f765b7fc75752549a9bd15761fe1d856d102703a6264672452810888cc455fe4 SHA1: d79128edb6b47c75c816caa50ab26dbe87084a8f MD5sum: 57a2b41b47bf812e54b993cb3e044d68 Description: library for Condor's classads expression language (development) A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the static library and header files. Package: libclassad0 Source: classads Version: 1.0.9-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1048 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libs Filename: pool/main/c/classads/libclassad0_1.0.9-2~nd09.10+1_i386.deb Size: 423362 SHA256: 00556f7c71a43a954b97c28fc7b23968437267bd63d847e5e03d405fdd39ffa9 SHA1: a98bf16ec9dc3b5d04da37cb122f8a8c42c05054 MD5sum: 25c8ac9862f8480a80dcc5040424bb30 Description: library for Condor's classads expression language A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the runtime library. Package: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 208 Depends: libgiftiio0 (= 1.0.9-1~karmic.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~karmic.nd1_i386.deb Size: 62668 SHA256: b9c3952920cd7d058b4162abcf98fdf45fcc7ef596c31e01ece99e2eba513a24 SHA1: 3949868f921aeb6a8bbdd5949e2d739110446928 MD5sum: c37ce3cdbe126b2a9001002d296a1e04 Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the header files and static library. Package: libgiftiio0 Source: gifticlib Version: 1.0.9-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 180 Depends: libc6 (>= 2.4), libexpat1 (>= 1.95.8), libnifti2, zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libs Filename: pool/main/g/gifticlib/libgiftiio0_1.0.9-1~karmic.nd1_i386.deb Size: 57444 SHA256: ab60e9a9c8a05161152ee1b2d16dc446307c508627c66dd86aaed3e819e7c313 SHA1: 895b9edf40378f0e8d48859a9830f5921227bc9f MD5sum: 6ea63a23ce1ca074ff7c80ccbc5f48f0 Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package contains the shared library. Package: libhdf5-doc Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: all Maintainer: Debian GIS Project Installed-Size: 180 Suggests: libhdf5-dev, www-browser, pdf-viewer, doc-base Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: doc Filename: pool/main/h/hdf5/libhdf5-doc_1.8.3-2.1~karmic.nd1_all.deb Size: 77692 SHA256: 2737bc11333c2f06d9860bbc72a4cab735eba9c8f817f73b174d1252e9ebb76c SHA1: 2a3e364b8aaa5b2bd16b9fd581606f7f0ff330ba MD5sum: 0c214ff584add34d707746b752cbd589 Description: Hierarchical Data Format 5 (HDF5) - Documentation HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains documentation for HDF5. Package: libhdf5-lam-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 4612 Depends: libc6 (>= 2.7), liblam4, zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libs Filename: pool/main/h/hdf5/libhdf5-lam-1.8.3_1.8.3-2.1~karmic.nd1_i386.deb Size: 1035750 SHA256: 298d1601dbded6d02c9ac7b746456939fd09682d7eedae85bd3b73cee66abff6 SHA1: c728606df0667754ebd61bc7b7f5ba4b151ec0e8 MD5sum: de64c532d983f4759ef300e90737925c Description: Hierarchical Data Format 5 (HDF5) - runtime files - LAM version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for use with LAM. Package: libhdf5-lam-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 6552 Depends: libhdf5-lam-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg62-dev, lam4-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libdevel Filename: pool/main/h/hdf5/libhdf5-lam-dev_1.8.3-2.1~karmic.nd1_i386.deb Size: 1483270 SHA256: 506d47ea334b316a3ef1a33ac2ec7ce510a2ae7faa65157aa853da2361e27f42 SHA1: f97dff298fddbacefaca88c1ddd7a3e8d55051b6 MD5sum: 60e80491074842d14fe92cba73263000 Description: Hierarchical Data Format 5 (HDF5) - development files - LAM version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for use with LAM. Package: libhdf5-mpich-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 5416 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libs Filename: pool/main/h/hdf5/libhdf5-mpich-1.8.3_1.8.3-2.1~karmic.nd1_i386.deb Size: 1343432 SHA256: c3fb1efafad5697348cbf04428bc5eb48ce0533d33085b68bac4c792e4145b83 SHA1: 5eaa302e89ab557612de26a10467cad72aa0ebbb MD5sum: 796b90f180df70f8799dfa66ea40e9f4 Description: Hierarchical Data Format 5 (HDF5) - runtime files - MPICH version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for use with MPICH. Warning: the C++ interface is not provided for this version. Package: libhdf5-mpich-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 14648 Depends: libhdf5-mpich-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg62-dev, libmpich1.0-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libdevel Filename: pool/main/h/hdf5/libhdf5-mpich-dev_1.8.3-2.1~karmic.nd1_i386.deb Size: 2157508 SHA256: 4d574b01ae58daf29ca766514490b613bb3235f9ce11e272e511d609bca624ea SHA1: 51c8496ce3d7694ef5212835ddcedea778baac8a MD5sum: 4851c3f046077e419e72861ec00c1ab1 Description: Hierarchical Data Format 5 (HDF5) - development files - MPICH version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for use with MPICH. Warning: the C++ interface is not provided for this version. Package: libhdf5-openmpi-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 4876 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libopenmpi1.3, zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libs Filename: pool/main/h/hdf5/libhdf5-openmpi-1.8.3_1.8.3-2.1~karmic.nd1_i386.deb Size: 1110706 SHA256: 3ab2c0e4f40ff0f77ac209d76a2635da9935235fc2bd2d672fb5a56fd9a7aaf6 SHA1: 087850d11d1feb1a4f1683117880c726904270a4 MD5sum: 63aad1b646b9552c83d71c580289d4f6 Description: Hierarchical Data Format 5 (HDF5) - runtime files - OpenMPI version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for use with OpenMPI. Package: libhdf5-openmpi-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 14648 Depends: libhdf5-openmpi-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg62-dev, libopenmpi-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libdevel Filename: pool/main/h/hdf5/libhdf5-openmpi-dev_1.8.3-2.1~karmic.nd1_i386.deb Size: 2158316 SHA256: 4a016f4d43bc2ad3bc69583dee19b03782a2c6ba871acc52ae3096b9d782748f SHA1: ff3881934a8f44fe8099bf42e54706723173b9d9 MD5sum: 00fdd47988731ffb5054337cae7e155f Description: Hierarchical Data Format 5 (HDF5) - development files - OpenMPI version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for use with OpenMPI. Package: libhdf5-serial-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 5176 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: libs Filename: pool/main/h/hdf5/libhdf5-serial-1.8.3_1.8.3-2.1~karmic.nd1_i386.deb Size: 1212626 SHA256: 007ec637afa2072994c906212b5adedc4577b76d41082b73a563ac7bb0ed7614 SHA1: 34e075852b6d5f007c533fe3afac32bcd2233391 MD5sum: 40edaacb1df66afca414d4e65b4b673f Description: Hierarchical Data Format 5 (HDF5) - runtime files - serial version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for serial platforms. Package: libhdf5-serial-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: i386 Maintainer: Debian GIS Project Installed-Size: 14972 Depends: libhdf5-serial-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: libdevel Filename: pool/main/h/hdf5/libhdf5-serial-dev_1.8.3-2.1~karmic.nd1_i386.deb Size: 2261026 SHA256: 3697ca2cdf7ba7b4577e4fddc946c6012cf9d636fce53297081eeb879e6693d8 SHA1: 8d45d0a14b8e0a5a9eadc35e73cb7c89eccd1c78 MD5sum: cf6faa39f1fb36969439a3f9ed602f09 Description: Hierarchical Data Format 5 (HDF5) - development files - serial version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for serial platforms. Package: libnifti-dev Source: nifticlib Version: 2.0.0-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 464 Depends: libnifti2 (= 2.0.0-1~karmic.nd1) Conflicts: libfslio-dev, libnifti0-dev, libnifti1-dev, libniftiio-dev Replaces: libnifti1-dev Homepage: http://niftilib.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/n/nifticlib/libnifti-dev_2.0.0-1~karmic.nd1_i386.deb Size: 151636 SHA256: 189a9fcf6801263b46fa37264eff33da4f90cbb20a56fa695e3bd38bb817e570 SHA1: 1438cec40dc92e4dd875621fb4d15a6f4bae3bbc MD5sum: 5e9fc441119710428886e7fb4fb604a7 Description: IO libraries for the NIfTI-1 data format 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 header files and static libraries of libniftiio, znzlib and libnifticdf. Package: libnifti-doc Source: nifticlib Version: 2.0.0-1~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1936 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~karmic.nd1_all.deb Size: 248616 SHA256: 70fcdfd2c371ab527325dbec01e9ce5fe1d34e3dc931473e2c4960ef548deadb SHA1: 41895f75d06ec3f194458113aa48000a3f0143e2 MD5sum: 4328fe117b4decbbc34896aca931475d 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: libnifti2 Source: nifticlib Version: 2.0.0-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 304 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-1~karmic.nd1_i386.deb Size: 107460 SHA256: 50294fb966b0f8ffd999ca381ebd893fe84333bf8fedd0b2abb9a204178feca6 SHA1: c88a8fe2ebe028e70726ada2623a003cce599e37 MD5sum: d7aa1664676d5c04c011b366910a3b0a Description: IO libraries for the NIfTI-1 data format 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 contains the shared library of the low-level IO library niftiio, low-level IO library znzlib and the nifticdf shared library that provides functions to compute cumulative distributions and their inverses. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-2~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 276 Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-2~karmic.nd1_i386.deb Size: 43858 SHA256: 667349b6764e5bc806ad67123020a8a2f70d0bd502e9ed15a34f047d89d7b599 SHA1: 8fd768c0da68a90da089c1b7dc2e582abc5fe263 MD5sum: af608f79cc306d8836bbbe78692de276 Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides static libraries and header files. Package: libopenmeeg1 Source: openmeeg Version: 2.0.0.dfsg-2~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 828 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libmatio0, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/libopenmeeg1_2.0.0.dfsg-2~karmic.nd1_i386.deb Size: 232422 SHA256: 4c40211e577b85c607a35c933f919abd8913bb8d63e9c58bee6a5ca9b1452598 SHA1: f6662020ea4c741445ce6658a047a1773622ec1a MD5sum: b159bd0dd433b5916c62439293e63517 Description: library for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. Package: libsvm-dev Source: libsvm Version: 3.0-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd09.10+1) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: libdevel Filename: pool/main/libs/libsvm/libsvm-dev_3.0-1~nd09.10+1_i386.deb Size: 28204 SHA256: db0d1452ba8333dea7a21770dc2dff1c2c82b7872226427390ae6b25767b14b3 SHA1: 72de25b08c5b2b30cdca577730f45c9ff4ba6e6b MD5sum: b08207bc1a2755f1cfd6e64c89b96429 Description: The LIBSVM header files These are the header files for LIBSVM, a machine-learning library. Package: libsvm-java Source: libsvm Version: 3.0-1~nd09.10+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~nd09.10+1_all.deb Size: 2074 SHA256: cdf17c51b02bca27137c4e2f14f39645275d8351d137c80f172ef143fab499b0 SHA1: 5d8670a12f843ef9fa94dc01b7afc48937a9d7d9 MD5sum: 1549a4e0c9a241a9bc35d633fe936274 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: libsvm-tools Source: libsvm Version: 3.0-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 328 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python, gnuplot Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: devel Filename: pool/main/libs/libsvm/libsvm-tools_3.0-1~nd09.10+1_i386.deb Size: 112696 SHA256: 11d45ba43244a527c9c2622fd69e48cb19a0065b304082abf74084a4ffb4a4d5 SHA1: 512fb90bf55302b6bc974b8afb5c6150030dd83d MD5sum: 214a086bed2519c73e9a32a1b60758e0 Description: The LIBSVM binary tools LIBSVM is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection. LIBSVM homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm Package: libsvm3 Source: libsvm Version: 3.0-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 120 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: libs Filename: pool/main/libs/libsvm/libsvm3_3.0-1~nd09.10+1_i386.deb Size: 43174 SHA256: 96d1e9e1498217ce9a03539dc22a34092fe3d332a6d3c2dcb2a9656300691adf SHA1: ef3e797f5b649ebbc8dd46150fcd43ede7d347e6 MD5sum: 3d37f14633183bb09aff2e45b65653ff Description: library implementing support vector machines The LIBSVM library is used to calculate Support Vector Machine optimizations with a great variety of powerful options. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. It is primarily of interest to machine-learning researchers and artificial intelligence application developers. Package: libsvm3-java Source: libsvm Version: 3.0-1~nd09.10+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~nd09.10+1_all.deb Size: 60488 SHA256: b0f1418c67d104784deb2238442de6d5f14ed6430b07dd7d1a6e3baf9b2860ac SHA1: 84096d101370a6ae234fd43ccb1273c9f0c2c303 MD5sum: d79ad9bd2a574cd19033d5553bb46d11 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: matlab-support-dev Source: matlab-support Version: 0.0.14~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 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_0.0.14~nd09.10+1_all.deb Size: 5468 SHA256: afe3f33e4f3ec28e5ed8573884a68aafbc2554b2eeee822452896be8c936b0d1 SHA1: 4e147b3a507ecde588de7badb0f1950567c791ca MD5sum: f1c5850ed14cbdd5d78fdac79b8d066a Description: helpers for packages building Matlab toolboxes Analogous to Octave a Makefile snippet is provided that configures the locations for architecture independent M-files, binary MEX-extensions, and there corresponding sources. This package can be used as a build-dependency by other packages shipping Matlab toolboxes. Package: mriconvert Version: 2.0-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2120 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.10.1), libwxgtk2.8-0 (>= 2.8.10.1) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0-2~nd09.10+1_i386.deb Size: 760158 SHA256: c1b3478b69854c76a50e6ecae440416d63063e19c5dd775d78db5c5a2bfe58df SHA1: 103e7569b318be8104b9217eee61fe0f47cf5760 MD5sum: 91db6f685fac9fec89f459b734c2673f Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5 , SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: mrtrix Version: 0.2.8-1~karmic.nd1 Architecture: i386 Maintainer: Michael Hanke Installed-Size: 6588 Depends: libatk1.0-0 (>= 1.20.0), libc6 (>= 2.4), libcairo2 (>= 1.2.4), libcairomm-1.0-1 (>= 1.6.4), libfontconfig1 (>= 2.4.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.22.0), libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1c2a (>= 1:2.18.0), libice6 (>= 1:1.0.0), libpango1.0-0 (>= 1.14.0), libpangomm-1.4-1 (>= 2.24.0), libsigc++-2.0-0c2a (>= 2.0.2), libsm6, libstdc++6 (>= 4.3), libx11-6, libxmu6, libxt6 Suggests: mrtrix-doc Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: science Filename: pool/main/m/mrtrix/mrtrix_0.2.8-1~karmic.nd1_i386.deb Size: 2257994 SHA256: bb2ff411c71a871dd8f97763affcda314c2f2933fc438414e52b4c97dcb21a05 SHA1: 3bcbe900aa4d691affd9138eaca8ccf7ddcc1037 MD5sum: 7e0be6d1162d0200e6b7f5dc5300d606 Description: diffusion-weighted MRI white matter tractography 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. Magenetic resonance images in DICOM or ANALYZE format are supported. Package: mrtrix-doc Source: mrtrix Version: 0.2.8-1~karmic.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 3416 Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.8-1~karmic.nd1_all.deb Size: 2949218 SHA256: 106ead56716b48fb7e92c1ef9d6cf382965316eb069e8e55344d05fe083592c5 SHA1: 589ec0ceb6e31aa04495af0c8c0a1922a5c95643 MD5sum: bc023f9116be1a0d2b00f20a70353548 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. Magenetic resonance images in DICOM or ANALYZE format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.24~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 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_0.24~nd09.10+1_all.deb Size: 114480 SHA256: 02dbb91db9a05e83bb635868b66d3208ea5b8f5cd65a218a0c24e9c1cad66f63 SHA1: bed24a0247651b29e32395d8d7ceba6ebc46003d MD5sum: 3892bc6b50f28c3799cd5c2af3142cc7 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: 0.24~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4400 Depends: devscripts, cowbuilder, python, neurodebian-keyring Recommends: virtualbox-ose, virtualbox-ose-fuse, zerofree Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.24~nd09.10+1_all.deb Size: 3794952 SHA256: 190394da7b788500de5227c506d08a22b9d487d074a03309bc9fb424650a6207 SHA1: f0437f68136f8f8ae3637bc84eec6a33b28678d1 MD5sum: 6f3a36306a72e036a34b5ef3c11873b0 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: 0.24~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 236 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm, 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_0.24~nd09.10+1_all.deb Size: 12710 SHA256: 89669a6c6783d9ed3d462c79d490517b73ac7687e68455cd6704733b35ca7684 SHA1: 1dd43022d94b8d8ef83b2e5234393d7dc8d718cc MD5sum: d062a329a37657979c4b0ebce414112c 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: 0.24~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 56 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.24~nd09.10+1_all.deb Size: 5790 SHA256: bfef7a7cca78b2e2de42dd2c53ab3f69b99a618e945e891a0959bfb0e0a43c2c SHA1: 9967b6ac3c68e6bc942893b89b102ba66c8578e5 MD5sum: e7158594deab37b9763ed506e355690f 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: 0.24~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.24~nd09.10+1_all.deb Size: 4956 SHA256: b871818938868809baf679b45f0c104fa66d7d1f55add48bcb68a6820cb4f60a SHA1: 1dade53af5b6ee07a4b29447c1ecf54f92953bfc MD5sum: 496cec1966fb50c2a189bd81a44713d5 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: nifti-bin Source: nifticlib Version: 2.0.0-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 200 Depends: libc6 (>= 2.7), libnifti2 Homepage: http://niftilib.sourceforge.net Priority: optional Section: utils Filename: pool/main/n/nifticlib/nifti-bin_2.0.0-1~karmic.nd1_i386.deb Size: 59280 SHA256: 38ec93cf9d37196ee1227b302e7283558c731c105172b33304022d11a8cdb444 SHA1: e5b4d0e03ab788b6d892c2039e1cbb6ee337dcb4 MD5sum: 1ab9b505c624d37856cca2269b12e4c8 Description: tools shipped with the NIfTI library 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 tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test). 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: octave-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1440 Depends: libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libhdf5-serial-1.8.3 | libhdf5-1.8.3, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.6+20071006-3), libreadline5 (>= 5.2), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_0.94.2+svn2552-1~pre1~nd09.10+1_i386.deb Size: 561942 SHA256: d6f31972aba92e2dee177bf0917c528a18550e90f7c7de01e0b86520fb3519f9 SHA1: 5c46b95abeedbe55ccfa0b0284db51386b20f48c MD5sum: 81d74f4024b67c608dc3730b748a5c18 Description: Octave bindings for BioSig library This package provides Octave bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 560 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libmatio0, libopenmeeg1, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/openmeeg-tools_2.0.0.dfsg-2~karmic.nd1_i386.deb Size: 153952 SHA256: 1f73ffcebe029d9e908781ad073af14ec462a5873f224ca4b5f25ef71fd0cd28 SHA1: c24408da1e46fea57fac47054ed10cb8f6b65db6 MD5sum: 9e33be1e654357401ab404726c45359d Description: tools for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides command line tools. Package: opensesame Version: 0.22+git9-g8633c14-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2504 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~) Recommends: python-serial (>= 2.3~) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.22+git9-g8633c14-1~nd09.10+1_all.deb Size: 711502 SHA256: 9aff2671504580907e9f78b08e660cf46d5e0acc613542e0754ff13deefc4fb5 SHA1: 048b3f3793ef07a1e24d817dd82f540fb6323372 MD5sum: cb92cadd7b30a22c0ad55fe39eea5a16 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: psychopy Version: 1.63.04.dfsg-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4480 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-scipy, libavbin0, ipython Suggests: python-iolabs Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.63.04.dfsg-1~nd09.10+1_all.deb Size: 2370890 SHA256: 5cc000caee39a116f3e2995850ff26a42606659dd6e1cddd95cd8da94aa4842e SHA1: cb99de53be4f2de355443488382cffb61904c543 MD5sum: 3bcb3212d8fa34d2c1cb8c65928412de 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: python-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 912 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_0.94.2+svn2552-1~pre1~nd09.10+1_i386.deb Size: 324840 SHA256: 1c2a9088b9aaf0c1961ef36bdbcaad6c6d716cbd783f85053fa8bb7876660294 SHA1: 987572560f8247af352d93f68c7c6f073e2221dd MD5sum: 53b4a76e365b9226c5576e4d4d56ad44 Description: Python bindings for BioSig library This package provides Python bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: python-brian Source: brian Version: 1.3.0-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1788 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.3.0-1~nd09.10+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.3.0-1~nd09.10+1_all.deb Size: 313340 SHA256: 5de84e0677f7e6663d84e8c7455bcc685b3c322230e59810c381c45234a65a7b SHA1: 65efeb9cd638492696f9823696d0d5d56e95661c MD5sum: 3d6cbb29e0aec8b3fabf16bd555c51cf 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: 1.3.0-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5444 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_1.3.0-1~nd09.10+1_all.deb Size: 1683540 SHA256: 553d7f62ea03cef10bc8749794ebfcc6ce31d3d7d78ac7bfd2405a90c843eb58 SHA1: 6bfb5591615fc5165ad81af1aaae575b751a94dd MD5sum: f7cd270889d796384cbf4de2cf04e907 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-brian-lib Source: brian Version: 1.3.0-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 212 Depends: neurodebian-popularity-contest, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.3.0-1~nd09.10+1_i386.deb Size: 54404 SHA256: d24783236032fcfcaca1ecbb4d15a2e0751086b76c753e43a86f666895dc6334 SHA1: 822184600654e0020b4d3241b4305bd7f124ab0f MD5sum: 38606e9965c434908c5f58c68cb1aa57 Description: simulator for spiking neural networks -- extensions Brian is a clock-driven simulator for spiking neural networks. . This package provides Python binary extensions. Package: python-epydoc Source: epydoc Version: 3.0.1-4~karmic.nd1 Architecture: all Maintainer: Kenneth J. Pronovici Installed-Size: 1216 Depends: python (>= 2.1), python-support (>= 0.90.0) Recommends: gs-common, python-tk, python-docutils, texlive-latex-base, texlive-latex-extra, texlive-latex-recommended, texlive-fonts-recommended, graphviz Suggests: epydoc-doc, python-profiler Conflicts: python2.1-epydoc (<< 2.0-2), python2.2-epydoc (<< 2.0-2), python2.3-epydoc (<< 2.0-2) Replaces: python2.1-epydoc (<< 2.0-2), python2.2-epydoc (<< 2.0-2), python2.3-epydoc (<< 2.0-2) Priority: optional Section: python Filename: pool/main/e/epydoc/python-epydoc_3.0.1-4~karmic.nd1_all.deb Size: 267136 SHA256: 13e41553f4f85dfeb727bd64f5d41db7b740a6a2a1f11c9d13daf4e2514773cf SHA1: dbfd01805efa7940210f3c61c5f55abf718a3612 MD5sum: 6d547d3365ce2106f59f7722771fff68 Description: tool for generating Python API documentation Epydoc is a tool for generating API documentation for Python modules based on their docstrings. A lightweight markup language called epytext can be used to format docstrings and to add information about specific fields, such as parameters and instance variables. Epydoc also understands docstrings written in ReStructuredText, Javadoc, and plaintext. . This package contains the epydoc and epydocgui commands, their manpages, and their associated Python modules. Package: python-jinja2 Source: jinja2 Version: 2.3.1-1~nd09.10+2 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1008 Depends: neurodebian-popularity-contest, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), libc6 (>= 2.3.6-6~) Recommends: python-pkg-resources Suggests: python-jinja2-doc Enhances: python-pybabel Provides: python2.5-jinja2, python2.6-jinja2 Homepage: http://jinja.pocoo.org/2/ Priority: optional Section: python Filename: pool/main/j/jinja2/python-jinja2_2.3.1-1~nd09.10+2_i386.deb Size: 161136 SHA256: 3d185733efb40cbdff430941f729d0894541f7d10397b6ebe23c2cf1d439a9f5 SHA1: 28f0e7ee52eb5eee541cd25b9520e9faffc8aba4 MD5sum: bacade30f5a239621d663b879348242e Description: small but fast and easy to use stand-alone template engine Jinja2 is a template engine written in pure Python. It provides a Django inspired non-XML syntax but supports inline expressions and an optional sandboxed environment. . The key-features are: * Configurable syntax. If you are generating LaTeX or other formats with Jinja2 you can change the delimiters to something that integrates better into the LaTeX markup. * Fast. While performance is not the primarily target of Jinja2 it’s surprisingly fast. The overhead compared to regular Python code was reduced to the very minimum. * Easy to debug. Jinja2 integrates directly into the Python traceback system which allows you to debug Jinja2 templates with regular Python debugging helpers. * Secure. It’s possible to evaluate untrusted template code if the optional sandbox is enabled. This allows Jinja2 to be used as templating language for applications where users may modify the template design. Python-Version: 2.5, 2.6 Package: python-jinja2-dbg Source: jinja2 Version: 2.3.1-1~nd09.10+2 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 92 Depends: neurodebian-popularity-contest, python-jinja2 (= 2.3.1-1~nd09.10+2), python-dbg, libc6 (>= 2.3.6-6~) Provides: python2.5-jinja2-dbg, python2.6-jinja2-dbg Homepage: http://jinja.pocoo.org/2/ Priority: extra Section: debug Filename: pool/main/j/jinja2/python-jinja2-dbg_2.3.1-1~nd09.10+2_i386.deb Size: 23378 SHA256: b8ea98a405159977a80b547606d9e84a7c627dd9c1832c7afa938793ff0b87da SHA1: 630c9d34e3bdb062e1359ddfece5757b7f5e8d51 MD5sum: 6caff1a6616f852de9c0c901a80bd117 Description: small but fast and easy to use stand-alone template engine Jinja2 is a template engine written in pure Python. It provides a Django inspired non-XML syntax but supports inline expressions and an optional sandboxed environment. . This package contains the extension built for the Python debug interpreter. Python-Version: 2.5, 2.6 Package: python-jinja2-doc Source: jinja2 Version: 2.3.1-1~nd09.10+2 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 132 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-jinja2 Homepage: http://jinja.pocoo.org/2/ Priority: extra Section: doc Filename: pool/main/j/jinja2/python-jinja2-doc_2.3.1-1~nd09.10+2_all.deb Size: 15730 SHA256: 4ded24be0f14e08a1c4b12d52a717b09e37ad7b3efc6a13b4956238ec07b24b0 SHA1: 403fcde28529501de28adb9f8e2f0c5f20044b4c MD5sum: 888ad384831741e3ce55ada60b41d532 Description: documentation for the Jinja2 Python library Jinja2 is a small but fast and easy to use stand-alone template engine . This package contains the documentation for Jinja2 in HTML and reStructuredText formats. Package: python-joblib Source: joblib Version: 0.4.6-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 244 Depends: 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_0.4.6-1~nd09.10+1_all.deb Size: 38594 SHA256: c66bb7e714028c238b54cc1be8c93323bdfa97117ac78747ddd9c6f8829ea439 SHA1: 60a3833b774c0762595ebb03a5409f9ec6e205ed MD5sum: b419b08e4ce961bb35c9950e4a5eb1b2 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-libsvm Source: libsvm Version: 3.0-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd09.10+1), python-support (>= 0.90.0) Provides: python2.5-libsvm, python2.6-libsvm Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: python Filename: pool/main/libs/libsvm/python-libsvm_3.0-1~nd09.10+1_i386.deb Size: 7620 SHA256: 9d92baf8b5a1c77fe04d12368f246c8c8519312b75b0b000d740cd69e6b642c5 SHA1: a2dbf553794318d3aefcb01718865709fedcf876 MD5sum: 7b3218aaebd87c3bd9a114459ce0fdf1 Description: Python interface for support vector machine library Python interface for the LIBSVM library using ctypes. This new python interface is provided since 2.91, and it is incompatible with the old one. Package: python-mdp Source: mdp Version: 3.0+git8-g921253a-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1872 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, shogun-python-modular, python-libsvm, python-scikits-learn, python-joblib Suggests: python-pp, python-py Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.0+git8-g921253a-1~nd09.10+1_all.deb Size: 450940 SHA256: 6dd48f6bc3362a5f313dd76f7302bcab6467ea8576d050091e54a3665578712f SHA1: e52fde1312849fd9856e488a7cc505e9b8a504ba MD5sum: 3329dfe3c2021de259c789bd0679de3c Description: Modular toolkit for Data Processing Python data processing framework. 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. Package: python-mlpy Source: mlpy Version: 2.2.0~dfsg1-1~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 424 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mlpy-lib (>= 2.2.0~dfsg1-1~karmic.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~karmic.nd1_all.deb Size: 55824 SHA256: ca0d69d554ee8905fd2a75d63b72eabb3d67042c7f885963932a10023a73bd9b SHA1: 7fc5ed77711f394cb2a64c03bd9388c4bb802a8c MD5sum: f6aca6dbe7bbaf5f8262372a476a1d31 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~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1116 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~karmic.nd1_all.deb Size: 459648 SHA256: c925a4b0101ed4932c4be88fe0980143a66feedcc5cb5bbcdf4633863b0e3db9 SHA1: 8510d76b9f02db769439a7a6c6ad4825ce470a98 MD5sum: 12a947c4a44e6b6ea96abbf90642f9b9 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-mlpy-lib Source: mlpy Version: 2.2.0~dfsg1-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 524 Depends: libc6 (>= 2.3.6-6~), libgsl0ldbl (>= 1.9), python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Provides: python2.5-mlpy-lib, python2.6-mlpy-lib Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy-lib_2.2.0~dfsg1-1~karmic.nd1_i386.deb Size: 120316 SHA256: 3d21677896139b66c5d1aad7478ecd922a256f089a67d86fa034029c42ba94cb SHA1: c35aad187f7a7f1fab43d2cbe2f18cad70a4d15b MD5sum: 9be15ff94f16e2c9583426642cf2b925 Description: low-level implementations and bindings for mlpy This is an add-on package for the mlpy providing compiled core functionality. Python-Version: 2.5, 2.6 Package: python-mock Version: 0.6.0-1.1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 312 Depends: neurodebian-popularity-contest, python-support (>= 0.90.0) Recommends: libjs-jquery Provides: python2.5-mock, python2.6-mock Homepage: http://www.voidspace.org.uk/python/mock/ Priority: extra Section: python Filename: pool/main/p/python-mock/python-mock_0.6.0-1.1~nd09.10+1_all.deb Size: 57106 SHA256: 38d1ed016142de9a985c91f26bbe180150822fecc26192e4e4ce93a3db3b574a SHA1: d3b30dda6545b5e48bceb23fa6cbf46b4b888678 MD5sum: 6693ce07fc866c3967b5b9685f86be50 Description: Mocking and Testing Library mock provides a core mock.Mock class that is intended to reduce the need to create a host of trivial stubs throughout your test suite. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set specific attributes in the normal way. Package: python-mpi4py Source: mpi4py Version: 1.2.2-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest Recommends: openmpi-bin Suggests: python-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py_1.2.2-1~pre1~nd09.10+1_i386.deb Size: 8192 SHA256: 21fab5d287d5672b02c10b4700369edf51170abe6a7f9580802c3ce0c39f6d48 SHA1: 4ede0485a949ec074078c5e2fe1a1bd81b19b4f3 MD5sum: 15871bfdab9bc2c62de43b14e52b51af Description: bindings of the Message Passing Interface (MPI) standard 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). Package: python-mpi4py-dbg Source: mpi4py Version: 1.2.2-1~pre1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.2.2-1~pre1~nd09.10+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py-dbg_1.2.2-1~pre1~nd09.10+1_i386.deb Size: 6078 SHA256: 880aba9a06f7f4a4d9421f3ffc34ec7f2e9fc7325f62b05a05292886c4eadaad SHA1: a8bc45c8fb8892c5f349d66dee7a0cb27fcad267 MD5sum: cd86f15cd27ad114e8974f932b1d6f3f Description: bindings of the MPI standard -- debug symbols 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 debug symbols. Package: python-mpi4py-doc Source: mpi4py Version: 1.2.2-1~pre1~nd09.10+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~nd09.10+1_all.deb Size: 54788 SHA256: 5638f4517698a9e0c8236a0ed683fde6ef434b4d8540897fcc69e74327a3ff2d SHA1: 90889a8cfa5a30fdb318b1cbe2d33b9e4f6b6542 MD5sum: 96ba7d79a342be1ea7c64c0f7ae75381 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: 0.4.7-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4060 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.7-1~nd09.10+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.5-mvpa, python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.7-1~nd09.10+1_all.deb Size: 2187906 SHA256: c1c3d51750916440f8f0f320e9138067f6733480d73a25f96025988ef5b8930f SHA1: 71aa20f8fc7dc2b798d5bc540ca9d0f342dd270c MD5sum: 98d592d0eb6c7d884baf50cd2627cac0 Description: multivariate pattern analysis with Python Python module to ease pattern classification analyses of large datasets. 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: 0.4.7-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40608 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_0.4.7-1~nd09.10+1_all.deb Size: 8677482 SHA256: a6165067b9c87e9784b18f59976f6e6630d59bd905c4a97170686c717adb77b8 SHA1: 4be2f8a1cc4e00e16f1111e647c10f035b61dedd MD5sum: b9f59a5b3fe9921b1a3564eeeebf50be 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-mvpa-lib Source: pymvpa Version: 0.4.7-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 200 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Provides: python2.5-mvpa-lib, python2.6-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.7-1~nd09.10+1_i386.deb Size: 60564 SHA256: 3372391ee422ed03dc7755344dacda7b8258c0e3bd3970d2f72b13487648b6f4 SHA1: d7e8fed050f0beff7fde1506f8f0fc351de7ad4d MD5sum: 6cba52a95db677f04cc8a0781e66b2f0 Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. Python-Version: 2.5, 2.6 Package: python-mvpa-snapshot Source: pymvpa-snapshot Version: 0.5.0.dev+783+gde39-1~karmic.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 4420 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-snapshot-lib (>= 0.5.0.dev+783+gde39-1~karmic.nd1) 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-scikits-openopt, python-rpy, python-mvpa-doc Conflicts: python-mvpa Provides: python2.5-mvpa-snapshot, python2.6-mvpa-snapshot Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot_0.5.0.dev+783+gde39-1~karmic.nd1_all.deb Size: 2215394 SHA256: e984192ba7133d5be41938da8cc04a319aebaf2a4575df4aeca345642b696316 SHA1: 4a236f2879eb5da14c7e2bf147c5afb5effaac23 MD5sum: 5f91883019b4bdb36f3dc1264ddd6e6c Description: multivariate pattern analysis with Python Python module to ease pattern classification analyses of large datasets. 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 a development snaphot. The latest released version is provided by the python-mvpa package. Python-Version: 2.5, 2.6 Package: python-mvpa-snapshot-lib Source: pymvpa-snapshot Version: 0.5.0.dev+783+gde39-1~karmic.nd1 Architecture: i386 Maintainer: Experimental Psychology Maintainers Installed-Size: 280 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Conflicts: python-mvpa-lib Provides: python2.5-mvpa-snapshot-lib, python2.6-mvpa-snapshot-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot-lib_0.5.0.dev+783+gde39-1~karmic.nd1_i386.deb Size: 58480 SHA256: 344086c4bf8c66253e4a122ccd28199c3079908b532c4a74a682a2bcde4257ee SHA1: c382fca743f4ce7fe84ca77e2b9a33fbe968baf2 MD5sum: e0eab9eaed32d6a5fb0948064b7d176c Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. . This is a package of a development snaphot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.5, 2.6 Package: python-networkx Version: 1.1-2~karmic.nd1 Architecture: all Maintainer: Debian Python Modules Team Installed-Size: 2628 Depends: python (>= 2.5), 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_1.1-2~karmic.nd1_all.deb Size: 679700 SHA256: 20006cf6cb411bc1ead33ecd9ae40565e1d028f4831e0d34065d33aa850bb7b8 SHA1: 2f91b854f12ef9e7b6dbe71cc5f05814f9daad7c MD5sum: 1a81a8905ee51f5b2d59b8d587aae3f6 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 we mean a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network we usually mean 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-nibabel-snapshot Source: nibabel-snapshot Version: 1.0.0.dev+137+gf1c6-1~karmic.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~karmic.nd1_all.deb Size: 469796 SHA256: 806db3453ecc94ec1c0083c32cd29bf5eea6fb3b931e930e8f709c43adb51f80 SHA1: 6c791fad36c9735b47ea8a945ba4e887b8609174 MD5sum: 3387700332b502442b780ad2f36a3600 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-nifti Source: pynifti Version: 0.20100607.1-2~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1292 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libnifti2, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, libjs-jquery Provides: python2.5-nifti, python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-2~nd09.10+1_i386.deb Size: 339968 SHA256: 0d8235e526ed16edb5fa87199a45533698726461d99f587597d1ab2b1f933c7a SHA1: bdc035c7efbc02432f06914f031c3ef2228a6ccb MD5sum: 0c54e2b563c796dbd824de4a8675066f Description: Python interface to the NIfTI I/O libraries Using PyNIfTI one can easily read and write NIfTI and ANALYZE images from within Python. The NiftiImage class provides Python-style access to the full header information. Image data is made available via NumPy arrays. Python-Version: 2.5, 2.6 Package: python-nipy Source: nipy Version: 0.1.2+20110114-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4588 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.5, python-scipy, python-numpy (>= 1.2), python-nifti (>> 0.20090302), python-nipy-lib (>= 0.1.2+20110114-1~nd09.10+1) Recommends: python-matplotlib, mayavi2 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_0.1.2+20110114-1~nd09.10+1_all.deb Size: 1165282 SHA256: 38a60b2bc61690a5de16576f86e19da30077db4aa3b0099be8b46dadc3ec652d SHA1: 6ae4415b649235371a1e974b80960a75dee19f64 MD5sum: 631b0d135dcd39311528ca53b569b0c2 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI). Python-Version: 2.5, 2.6 Package: python-nipy-doc Source: nipy Version: 0.1.2+20110114-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11000 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_0.1.2+20110114-1~nd09.10+1_all.deb Size: 2753886 SHA256: 26112eb1f6f9efd745d0cc459301be924db46273277e24b635125f7a38b7abff SHA1: 31188e9203155991266fa67950dcf8fe906ceabb MD5sum: 8985a9a320f9a4ef46397129c9db895b Description: documention and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipy-lib Source: nipy Version: 0.1.2+20110114-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3004 Depends: neurodebian-popularity-contest, libatlas3gf-base | libatlas.so.3gf, libc6 (>= 2.4), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0) Provides: python2.5-nipy-lib, python2.6-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.1.2+20110114-1~nd09.10+1_i386.deb Size: 1026448 SHA256: fc5cc294f54ca3891c731698f0c4f75827630f23118693cb454d1971dc0a9e76 SHA1: 3e2fca57ff6c3d0fa3694927e935adfd179e2e95 MD5sum: e0307ffea9201fbee75856bec41a745f Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI). . This package provides architecture-dependent builds of the libraries. Python-Version: 2.5, 2.6 Package: python-nipype Source: nipype Version: 0.3.3-1~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1752 Depends: python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits Recommends: python-nifti, ipython, python-nose, python-networkx Suggests: fsl, afni, lipsia, python-nipy Provides: python2.5-nipype, python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.3.3-1~karmic.nd1_all.deb Size: 277562 SHA256: 22693d2484e104e0a30f54175331b7539c23edf3d6a2e8af330f78612524cdd2 SHA1: 32f7f24bf1b5da14275f2d08d3f8f4182eeca46c MD5sum: c3a23a5fdcc7efdfe9b430f756451043 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: 0.3.3-1~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3640 Depends: libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.3.3-1~karmic.nd1_all.deb Size: 840494 SHA256: c9a78de04fb849e111493d90273e1e84eace1be20c7d8fdc80e26450631fa598 SHA1: 89eae0d223fda3a96d2c76928fa3e4c332d64d72 MD5sum: ed4627d885cc60de192b880b67532b87 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-openmeeg Source: openmeeg Version: 2.0.0.dfsg-2~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 560 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libmatio0, libopenmeeg1, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-openmeeg Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: python Filename: pool/main/o/openmeeg/python-openmeeg_2.0.0.dfsg-2~karmic.nd1_i386.deb Size: 151860 SHA256: af1818d6cefb5cd4f59426b6020801e8380db6fc53377163b650caa57f8109aa SHA1: 71d4a6e050881a401644fbaa0bc2c30830c2c485 MD5sum: d69cb3052702b89596f628885bae7744 Description: Python bindings for openmeeg library OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides Python bindings for OpenMEEG library. Python-Version: 2.6 Package: python-openpyxl Source: openpyxl Version: 1.5.0-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 412 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_1.5.0-1~nd09.10+1_all.deb Size: 57464 SHA256: 86ccccc8127b49c4a52a23a5067e629df7795f48ae7797cc2ad7429a906500a5 SHA1: 0cb618490ded9d6c030d6cea1b17abad74952e8e MD5sum: 95dfc6540546080184a326e4c7aca8b3 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-pyepl Source: pyepl Version: 1.1.0-3~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 2212 Depends: python (<< 2.7), python (>= 2.5), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~karmic.nd1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>> 1.0.18), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0, libsndfile1, libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.5-pyepl, python2.6-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0-3~karmic.nd1_i386.deb Size: 539592 SHA256: c4b71b149f040f995f856c02b64c99c48d4db525f408b7d90853d043caadee9e SHA1: dbad6c80a9fdcc6b4e9f52eb6632d1f8909af97b MD5sum: 8b103c6a78e93135b2d83efbc1b32880 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 PyEPL for supported versions of Python. Python-Version: 2.5, 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~karmic.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~karmic.nd1_all.deb Size: 817826 SHA256: af092b3f9d0c2d780438753bf914eb30ff4fe60993098f6187b82821040e1526 SHA1: 982c801e2e3bcfee1084c3289535f08343814ec5 MD5sum: 2ddece677353b6d2194715660c1a7120 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~nd09.10+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~nd09.10+1_all.deb Size: 972186 SHA256: 05ffe8699bc8e69fb00ae20a01c8c269a18030fab58dab84fd7a895e36b67681 SHA1: 1dc3bd704d69a0bc64079c4d2c3de667fc4d1373 MD5sum: 71db49074636da298f2b6ee5c93a60af 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: 0.7.0-1~pre1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1020 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.0-1~pre1~nd09.10+1_all.deb Size: 187324 SHA256: 983ccdce67a43c89c19383e3c6470d19c7fb4182fc2bf9845cd495e0ebfb936f SHA1: bf076ffaad4a1eba7e6b12fce2af286634d274ae MD5sum: 4bb2d3a334b6fee93922f14440942818 Description: simulator-independent specification of neuronal network models PyNN allows to code for 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~karmic.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~karmic.nd1_all.deb Size: 6932 SHA256: f9f2c9a7770e3c0e6d0e2dfa1ddc06beb3f5ad09be3fdbfd09a3eef5a70a7c04 SHA1: 91ed70d1b6c3e7d24ae43314e04318afcfd6643b MD5sum: c1f6b1fadc5bdf8b71eea01444c41234 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-scikits-learn Source: scikit-learn Version: 0.7.1.dfsg-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1240 Depends: neurodebian-popularity-contest, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-scikits-learn-lib (>= 0.7.1.dfsg-1~nd09.10+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-scikits-learn-doc Provides: python2.5-scikits-learn, python2.6-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn_0.7.1.dfsg-1~nd09.10+1_all.deb Size: 270670 SHA256: 21761b06937b1f6e9b34c4e1b1e37fa34e6889c9373d2fae3089705a56d638e9 SHA1: 65620e287726e7132a85bfd9151e7ccfd45412be MD5sum: c3b9180b032fc74ad68178edd4d8f60f 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: 2.5, 2.6 Package: python-scikits-learn-doc Source: scikit-learn Version: 0.7.1.dfsg-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8992 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-scikits-learn-doc_0.7.1.dfsg-1~nd09.10+1_all.deb Size: 4409214 SHA256: 5d8da1716cf5dd2e58da5d7eb8d3a6d1b20125f0cd3f1240a25c235537107553 SHA1: 14ad0dd8afbd0877026cb3dd48d163e257c546fa MD5sum: 9053c1ce9366aa633d334bfdf53ffe25 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-scikits-learn. Package: python-scikits-learn-lib Source: scikit-learn Version: 0.7.1.dfsg-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2180 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Provides: python2.5-scikits-learn-lib, python2.6-scikits-learn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn-lib_0.7.1.dfsg-1~nd09.10+1_i386.deb Size: 783470 SHA256: 503ba8abe09f79e86c103d0e44c40a6b69c7d65399baf7c5dddb4ebdffe5c335 SHA1: db7c17d4bb6ebddbcf37e1dc1e6014b45327bf26 MD5sum: b07f8c80643987061e2aa72e824742d8 Description: low-level implementations and bindings for scikits-learn This is an add-on package for python-scikits-learn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.5, 2.6 Package: python-scikits-statsmodels Source: statsmodels Version: 0.2.0+bzr1990-1~karmic.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 9660 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Provides: python2.5-scikits-statsmodels, python2.6-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: python Filename: pool/main/s/statsmodels/python-scikits-statsmodels_0.2.0+bzr1990-1~karmic.nd1_all.deb Size: 1874616 SHA256: e32c4dee0c8a85900b5fdc393da58f91a05734bcdd23094f026dbe69471a100d SHA1: 770124df665eec0d3a1a5d463321a0f40eaaec74 MD5sum: 7aca46b5889f83bf61f9520cab6c987b Description: classes and functions for the estimation of statistical models scikits.statsmodels is a pure Python package that 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 avalable for each estimation problem. Python-Version: 2.5, 2.6 Package: python-scikits-statsmodels-doc Source: statsmodels Version: 0.2.0+bzr1990-1~karmic.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 2184 Depends: libjs-jquery Suggests: python-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: doc Filename: pool/main/s/statsmodels/python-scikits-statsmodels-doc_0.2.0+bzr1990-1~karmic.nd1_all.deb Size: 307420 SHA256: 55c4001d153835c75a39f780885be99133f19fa77658f35d9129b1943c2f3329 SHA1: c37233a396c7414a315d9f0485ce5bd8ea4c21c1 MD5sum: 5de75b1ff06ab92dcf4a5da3f01909ad Description: documentation and examples for python-scikits-statsmodels This package contains HTML documentation and example scripts for python-scikits-statsmodels. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd09.10+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~nd09.10+1_all.deb Size: 1260332 SHA256: f8317cd2487392e6e8d2f34ba1aa7d8a98e0a04f83cf0e388f9d93c2fa512c28 SHA1: c388380214ccf4718f4fbf7b06ce701cf7b6a581 MD5sum: d834d5de6b527a3e6c9721eeaafe9339 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-sympy Source: sympy Version: 0.6.7-1.1~nd09.10+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~nd09.10+1_all.deb Size: 1696254 SHA256: e0c154de2f019fe2dd8030c2c6a20770e4f635aa82c468e024e8d2c3443b904d SHA1: 14dac42d65b3b01d57256425263a9b0067b15053 MD5sum: dd0528f3afca28d783d728a9175033d3 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: spm8-common Source: spm8 Version: 8.4010~dfsg.1-4~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21124 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4010~dfsg.1-4~nd09.10+1_all.deb Size: 10087692 SHA256: 8063fa9e6267199ad818fcdc475dc4c9a39894aff28b049b6d47e48d7888197b SHA1: 155e5b6863babc181ff94542e65f7eb6bbd00692 MD5sum: 232c74af292bc8f92b10824f6a31a232 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: 8.4010~dfsg.1-4~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73316 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4010~dfsg.1-4~nd09.10+1_all.deb Size: 52168456 SHA256: 5bb1b364712fbdebefc0e8c5e8e62001756bfaf80a2b445fd882e4e921ea001a SHA1: 402c19cc1d03f6af1e3d75d2129c3c38ef2054a7 MD5sum: 7a8abd93784c25070698f9cc54bee88c 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: 8.4010~dfsg.1-4~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11732 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4010~dfsg.1-4~nd09.10+1_all.deb Size: 10842938 SHA256: f6ae25f6c4baf785112c2928a8761a87f65318408bd51c803f81150aa062d38d SHA1: f169494a8d6c26778305001dddc2b5071ee1df1a MD5sum: d0e4f55242154983899a6ca0afed798f 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: 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: voxbo Version: 1.8.5~svn1246-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 9812 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libfontconfig1 (>= 2.4.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4.5.1), libqt4-qt3support (>= 4.5.1), libqtcore4 (>= 4.5.1), libqtgui4 (>= 4.5.1), libstdc++6 (>= 4.4.0), libx11-6, libxext6, libxi6, libxrender1, zlib1g (>= 1:1.1.4) Suggests: mni-colin27-nifti, matlab-spm8 Homepage: http://www.voxbo.org Priority: extra Section: science Filename: pool/main/v/voxbo/voxbo_1.8.5~svn1246-1~nd09.10+1_i386.deb Size: 3672754 SHA256: 3b80f8cfc6aa06776e3570b726c55855a4c4e57f6cd4b1b151b5ecd92e8a0c8e SHA1: acb9e8df3ffcab33cb37aa21c60e9851361e4ead MD5sum: ee74d09e17c9da43ac2104b66dcf1dfa Description: processing, statistical analysis, and display of brain imaging data This is a toolkit for analysis of functional neuroimaging (chiefly fMRI) experiments and voxel-based lesion-behavior mapping. VoxBo supports the modified GLM (for autocorrelated data), as well as the standard GLM for non-autocorrelated data. The toolkit is designed to be interoperable with AFNI, FSL, SPM and others.