Package: ants Version: 1.9.2+svn680.dfsg-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 38212 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.18, libstdc++6 (>= 4.4.0) Suggests: fsl, gridengine-client Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_1.9.2+svn680.dfsg-1~nd10.10+1_amd64.deb Size: 11326468 SHA256: a4806d17a2d77578a6a8357d428b492f79fe46e24a684ad18414177f9bbba264 SHA1: cbd5567bc66694cca62b6778b785612bf501d9ed MD5sum: 5cb2c5d85e93d091fe74f38b363eea10 Description: advanced normalization tools for brain and image analysis Advanced Normalization Tools (ANTS) is an ITK-based suite of normalization, segmentation and template-building tools for quantitative morphometric analysis. Many of the ANTS registration tools are diffeomorphic, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). Package: autotools-dev Version: 20100122.1~nd10.10+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~nd10.10+1_all.deb Size: 73000 SHA256: 2e4624e33e50c6bcdb26ab691a4cb339ee5d9efb514cb80d1ed34f66a224c1f3 SHA1: 4e4eeda943b73962c622a9d7222ec12799d9ad66 MD5sum: 933c51b7e10a71382d16980f9246f2a5 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 664 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~nd10.10+1_amd64.deb Size: 255108 SHA256: 275e8d22af8d64ee770ac18ad97916c90c337694c1151a0e11e2c7ea819b5fc8 SHA1: f692e9536a86f40e6f99240310ebbd29d6ee4566 MD5sum: 8005dd850758356d766c75ba799bc545 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 19224 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-network (>= 4:4.5.3), libqt4-opengl (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvtk5.4, 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~nd10.10+1_amd64.deb Size: 7408074 SHA256: aa6a0e58ffaec1c432aa1153877fb9bf6bf2ccb59089bcfbdabf43327b3e93a0 SHA1: e44963c9ec2b61844f3bc4a25b9ddac0690be268 MD5sum: b7662378bc5209072cab0117474cf9e4 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: cctools Version: 3.3.0~svn1179-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2752 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfuse2 (>= 2.8.1), libglobus-common0, libglobus-gss-assist3, libglobus-gssapi-gsi4, libkrb5-3 (>= 1.6.dfsg.2), libstdc++6 (>= 4.1.1) Suggests: cctools-doc Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: utils Filename: pool/main/c/cctools/cctools_3.3.0~svn1179-1~nd10.10+1_amd64.deb Size: 1112832 SHA256: bf814772a5892ccaca99fec819844c57c9f0c7b096b93ca4866b6d6acb1cb403 SHA1: a056ad29f94d99e2297a6cbf128faaed804dbcde MD5sum: cd17d2cfdb60ce9afa6d84ca67e96577 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: cctools-dev Source: cctools Version: 3.3.0~svn1179-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1076 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: libs Filename: pool/main/c/cctools/cctools-dev_3.3.0~svn1179-1~nd10.10+1_amd64.deb Size: 225348 SHA256: f2630d65bffc13d8109f58777f38e561faddbd696ab732594bc307065488331b SHA1: 1c9a1a023a9e38381a19ced3de49b1ec14ca169b MD5sum: 499acb9cdaf879bc21f3fa5359b5f88b Description: libraries and header files for cctools cctools is 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: cctools-doc Source: cctools Version: 3.3.0~svn1179-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2384 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/cctools-doc_3.3.0~svn1179-1~nd10.10+1_all.deb Size: 258946 SHA256: e80baedd02865440ef1da4b2cebb2c2176dcba34812e2b18ce7b5befe3ee6199 SHA1: eed0575daf05c3017fca87abf665b09a5fc1ba2c MD5sum: d4cfea026daa8a78dfc3afecd817d2b3 Description: documentation for cctools cctools is 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: classads Version: 1.0.9-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 116 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libclassad0 (= 1.0.9-2~nd10.10+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~nd10.10+1_amd64.deb Size: 30202 SHA256: 68952720a2d500e7b82fc78af464f237dc688daa8a6d38e0f57e461cc58bb7ea SHA1: 3c73e666f66eabb9d118871b2b8d2ba6f7ac7778 MD5sum: b22e59d0860852823221287721aabb4f 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: condor Version: 7.5.5+git995-ga9a0d2a-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11716 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.8), libclassad0, libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2-1), libexpat1 (>= 1.95.8), libgcc1 (>= 1:4.1.1), libglobus-callout0, libglobus-common0, libglobus-ftp-client2, libglobus-ftp-control1, libglobus-gass-transfer2, libglobus-gram-client3, libglobus-gram-protocol3, libglobus-gsi-callback0, libglobus-gsi-cert-utils0, libglobus-gsi-credential1, libglobus-gsi-openssl-error0, libglobus-gsi-proxy-core0, libglobus-gsi-proxy-ssl1, libglobus-gsi-sysconfig1, libglobus-gss-assist3, libglobus-gssapi-error2, libglobus-gssapi-gsi4, libglobus-io3, libglobus-openssl-module0, libglobus-rsl2, libglobus-xio0, libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.7dfsg), libkrb5support0 (>= 1.7dfsg~beta2), libldap-2.4-2 (>= 2.4.7), libltdl7 (>= 2.2.6b), libpcre3 (>= 7.7), libssl0.9.8 (>= 0.9.8m-1), libstdc++6 (>= 4.4.0), libvirt0 (>= 0.5.0), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), adduser Priority: extra Section: science Filename: pool/main/c/condor/condor_7.5.5+git995-ga9a0d2a-1~nd10.10+1_amd64.deb Size: 4374656 SHA256: 38e5504f891090ded96541aef2a35747b0f40997e40a21ccb770cc1d85864a00 SHA1: 5df729edc315acaf0365b1b460cbae76ae2e8f31 MD5sum: 4b1bc793d3e039dd72e9debcc9c866ea Description: workload management system Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor, Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing system, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . The Debian package uses Debconf to determine an appropriate initial configuration for a machine that shall join an existing Condor pool, and moreover, allows creating a "Personal" (single machine) Condor pool automatically. Package: condor-dbg Source: condor Version: 7.5.5+git995-ga9a0d2a-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 36260 Depends: neurodebian-popularity-contest, condor (= 7.5.5+git995-ga9a0d2a-1~nd10.10+1) Priority: extra Section: debug Filename: pool/main/c/condor/condor-dbg_7.5.5+git995-ga9a0d2a-1~nd10.10+1_amd64.deb Size: 12818430 SHA256: 93eb6b26481bb77df9841476c5ecbf5fe3b5ef163bf27da25d760fd88b9ec19a SHA1: 3d7824584ac6882da30523797447b407e29c5ba4 MD5sum: 4253ac8f73fe24ba3a512a4316dd9624 Description: debugging symbols for Condor Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor, Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing system, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This packages provides the debugging symbols for Condor. Package: condor-doc Source: condor Version: 7.5.5+git995-ga9a0d2a-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12292 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.5.5+git995-ga9a0d2a-1~nd10.10+1_all.deb Size: 6257236 SHA256: 54dc3a40dffd202843209bb7f5d47f8b2c6e924287d79ed973155dbcf5bb1758 SHA1: 755c02370aa9cfb7f67c343f7c5226a604495641 MD5sum: b454bd7591677438273bebe080565687 Description: documentation for Condor Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor, Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing system, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This packages provides Condor's documentation in HTML and PDF format, as well as configuration and other examples. Package: cython Version: 0.13-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3680 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd10.10+1_amd64.deb Size: 865752 SHA256: 2dcdbac1a90314879ef382ff5fa4ccf87630d47af9a78a4d2d2c255df192d884 SHA1: 0e747b0c464824debec67f456d27395da1616fea MD5sum: e033c32d40dd528dc4cfe07e08efa7f0 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: 2.6 Package: cython-dbg Source: cython Version: 0.13-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5324 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.3), cython (= 0.13-1~nd10.10+1) Suggests: gcc Homepage: http://cython.org/ Priority: extra Section: debug Filename: pool/main/c/cython/cython-dbg_0.13-1~nd10.10+1_amd64.deb Size: 1711982 SHA256: 0e22c074117ed14ab06670c17f8657bc20e8b7c881cc7048dbdacb071846be55 SHA1: 9c19b4e7b7e21c3a2c5783a72db0bf2ccb3320c5 MD5sum: 8d5323e51fb18f0fdc14809311b14ae0 Description: C-Extensions for Python (Debug Build of Cython) This package contains Cython libraries built against versions of Python configured with --pydebug. Python-Version: 2.6 Package: dicomnifti Version: 2.28.14-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 524 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti1 (>> 1.1.0-2), 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~maverick.nd1_amd64.deb Size: 158698 SHA256: 9de31342677564bbd2c46db4c101360ba62dc0859527112e462a106307bcab65 SHA1: bfd0234348776fde2a5f160fe8ee5dd7b30d1f84 MD5sum: 8bd2a7a3900e37490b2c03a1ea9b3083 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5) Provides: libeatmydata Homepage: https://launchpad.net/libeatmydata Priority: optional Section: utils Filename: pool/main/libe/libeatmydata/eatmydata_26-2~nd10.10+1_amd64.deb Size: 8170 SHA256: 68a067f3f6568320b30de47135a05791d0503f9ba4a53118baa2f5acf5c5daeb SHA1: 7f05d2bbae1138d94ba56a988d676e8e726b3543 MD5sum: 77af09647b3176efac4e22bc8dd44c81 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: fail2ban Version: 0.8.4+svn20110323-1~nd10.10+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~nd10.10+1_all.deb Size: 97968 SHA256: f8fb14c62e06cdd270edecbba55185610ee182a284467571086d8bb846ee0ca0 SHA1: ab194e62672c513f43d29d9e2c16823e15b33048 MD5sum: 95b6a2d520b238d58fd25367aced3c7d 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: freenect Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, libfreenect-demos, libfreenect-dev, libfreenect0.0 Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/freenect_0.0.1+20101211+1-2~nd10.10+1_amd64.deb Size: 1578 SHA256: 889392f92ec6b3dd6d1017cecfa379175b06f45f649fa4b9de7390ef4bfb161d SHA1: a4497980f86a194bd199e5a90ad03df786601f84 MD5sum: a10d1e12078cb7871670ee5704d0ffa2 Description: library for accessing Kinect USB camera -- meta package libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the meta package to install all components of the project. Package: fslview Version: 3.1.8+4.1.6-2~maverick.nd2 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4136 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.4.0), libvtk5.4, libvtk5.4-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~maverick.nd2_amd64.deb Size: 1511708 SHA256: 7617b502940dd1d0dcd8b8d409e2b71554b0fbbf2ef01ed26aa95556b8cf653e SHA1: d9145e95a4887b0a05bc363de080969d7a9f130d MD5sum: 4bc78a17396b1e4798ab64fdeced84f5 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~maverick.nd2 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~maverick.nd2_all.deb Size: 2378976 SHA256: f86a19e226efeeb0c052522258b201d7f6c822e4bd9f0e0816a75daa0778f782 SHA1: 7cf0703841a0c9fb0338253f749f6b668671db08 MD5sum: bd2d711aef575e4963a60637ae6ffc0b Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gdf-tools Source: libgdf Version: 0.1.1~svn62-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 156 Depends: neurodebian-popularity-contest, libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-program-options1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: utils Filename: pool/main/libg/libgdf/gdf-tools_0.1.1~svn62-1~nd10.10+1_amd64.deb Size: 38522 SHA256: 5821636aee0db361f1ea22915a6541db36b7eae636611b3a01e28f583a1e4c85 SHA1: 31300008b550901a6cda1597b9dded4474cb1206 MD5sum: dcf99d705d8e3f4ebf8383f2c112b979 Description: IO library for the GDF -- helper tools GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the tool shipped with the library (gdf_merger). Package: gifti-bin Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 128 Depends: libc6 (>= 2.3.4), libexpat1 (>= 1.95.8), libgiftiio0, libnifti1 (>> 1.1.0-2), 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~maverick.nd1_amd64.deb Size: 29248 SHA256: 0e3f17a174bdf3fbc004c4c8b3fea6b1de90d5e745355e9907eecccc3c45aa79 SHA1: 5a1f62520a7089fc0f7779a6aad302eb8e8623f9 MD5sum: 371cc7de70de99fd19a3b4aef2fb0940 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: itksnap Version: 2.1.4-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8592 Depends: libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.18, libstdc++6 (>= 4.4.0), libvtk5.4 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.1.4-1~nd10.10+1_amd64.deb Size: 3673860 SHA256: a79264a12d4f26931e648f643516ab9d0cc6a25b532010e341fa974afcf5f508 SHA1: 7f5a6e79ec0a526c58cc5468f8349cb265d02833 MD5sum: 4da72a2d0625b05005531ffee8a4276f 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1624 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd10.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.2+svn2552-1~pre1~nd10.10+1_amd64.deb Size: 382670 SHA256: 871bf6666953211708dcca961f72a81bc8a98facf2c01ac3534b3a61cfe615cc SHA1: f71070a24308498ccc00bf1f44354228e435289a MD5sum: d65a63646ba8687826a8722dfeeac8c5 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 888 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~nd10.10+1_amd64.deb Size: 301736 SHA256: caa3ed631176b58e1f2608e314958fbf735dc6022f073c47e942382226e8a3d4 SHA1: c1a5b5d05e71af1ad9db441e199be3631259d847 MD5sum: f82f4a255e0385c4130b73043df33252 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 712 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd10.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig0-dbg_0.94.2+svn2552-1~pre1~nd10.10+1_amd64.deb Size: 173484 SHA256: f3858e79acc1e19044461c637e68c7b4ab4f8f65c221599087ba4875d194034b SHA1: 724a774c869875c3843114c4113b9e399c47a04d MD5sum: 3fb116960a282ec362b9c09ac4aa2be0 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2228 Depends: neurodebian-popularity-contest, libclassad0 (= 1.0.9-2~nd10.10+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~nd10.10+1_amd64.deb Size: 571096 SHA256: 43d45c236270952737227c9fbd40ab4687bea46a93862d88eb703c8f6ae36010 SHA1: f028bad12b614652ca6ad49bdaa246e49841ab52 MD5sum: 4d88f1e11e0ef603cac23f6adca5780a 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1060 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~nd10.10+1_amd64.deb Size: 420736 SHA256: 4e97eb772a263c3966aaea8bba96b532969e2f9df49fb1d7b0608c5116b2626b SHA1: d3754566b5f39b91eee1447d928760b8c4fb100a MD5sum: 8d00ad8adc4dd66e7e3025075bdc0090 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: libfreenect-demos Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.3.4), libfreenect0.0, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libice6 (>= 1:1.0.0), libsm6, libstdc++6 (>= 4.4.0), libusb-1.0-0 (>= 2:1.0.8), libx11-6, libxext6, libxi6, libxmu6, libglut3 Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-demos_0.0.1+20101211+1-2~nd10.10+1_amd64.deb Size: 23764 SHA256: 9a7dd736759857bb33dc08d6c0b11281f3ae8ea58c3f68d8c98502e0b4c8ac09 SHA1: e19f98e4e59b856c1cbd99ba49096579444b870c MD5sum: 756aa998a9f87519175031925b3b2112 Description: library for accessing Kinect USB camera -- demonstrations libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package includes some example programs for kinect. All programs start with a freenec- prefix. Package: libfreenect-dev Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 120 Depends: neurodebian-popularity-contest, libusb-1.0-0-dev, libfreenect0.0 (= 1:0.0.1+20101211+1-2~nd10.10+1) Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-dev_0.0.1+20101211+1-2~nd10.10+1_amd64.deb Size: 21650 SHA256: 752103004e86500fe7d1636097bdfc02ca7fa35ba37907c73a676e31fa91e40c SHA1: 8d3dd7ef3917c557ae2e1c0494e96c00c6cd9fc8 MD5sum: 36996945aee69f4bf60ec99928c34418 Description: library for accessing Kinect USB camera -- development files libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the development package containing the libraries and header for software development with libfreenect. Package: libfreenect0.0 Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 136 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libusb-1.0-0 (>= 2:1.0.8), udev Conflicts: libfreenect Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.0_0.0.1+20101211+1-2~nd10.10+1_amd64.deb Size: 25830 SHA256: fba44030e39ad33e5a15737dddc05e7d5f53cdc30a5e5716889030a0e6830a15 SHA1: 540931bb30dc554114667f1afecf9238231b75a6 MD5sum: 38b4fef523829f2a8d0005c1cca8a89c Description: library for accessing Kinect USB camera libfreenect is the core library for accessing the Microsoft Kinect USB camera. Currently, the library supports access to: - RGB and Depth Images - Motors - Accelerometer - LED Package: libgdf-dev Source: libgdf Version: 0.1.1~svn62-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 172 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1~svn62-1~nd10.10+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libdevel Filename: pool/main/libg/libgdf/libgdf-dev_0.1.1~svn62-1~nd10.10+1_amd64.deb Size: 18012 SHA256: c0136f22e38b6bd4f568f193fa921ee27cc76385805f4e8ddf16c29e08267933 SHA1: 98b78e8595a745062d1055eee34271fcf7a3356f MD5sum: c22ba2e877944b9794882a3ac19e2bcf Description: IO library for the GDF -- development library GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the header files and static library. Package: libgdf0 Source: libgdf Version: 0.1.1~svn62-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 360 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libs Filename: pool/main/libg/libgdf/libgdf0_0.1.1~svn62-1~nd10.10+1_amd64.deb Size: 102508 SHA256: 0da61e13eeb9b481233fa80b9d39f403cbde1e7d572fac540e27730f8c2bd571 SHA1: 3e31ba6234961eb9aa5c3399d848d8e29915115d MD5sum: f1ffb79e7826a6ea810d6d16f2fcaf61 Description: IO library for the GDF (general dataformat for biosignals) GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package contains the shared library. Package: libgdf0-dbg Source: libgdf Version: 0.1.1~svn62-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4176 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1~svn62-1~nd10.10+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: debug Filename: pool/main/libg/libgdf/libgdf0-dbg_0.1.1~svn62-1~nd10.10+1_amd64.deb Size: 1113332 SHA256: ba86639b221de417aa6da690bb81b55e848d14f1ebd947b87992e02b979f7988 SHA1: 35ac2e5d8234c3e29b2e7f75b9274933d38c4a97 MD5sum: 00ea07aa3640ebcf3ad3fab9604edcb3 Description: IO library for the GDF -- debug symbols GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides debug symbols. Package: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 256 Depends: libgiftiio0 (= 1.0.9-1~maverick.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~maverick.nd1_amd64.deb Size: 65020 SHA256: a5e63ac7d35ef813eed1d81d98075530acbe2f058a945bd212566b4d468bcfc2 SHA1: 47c3332fe15460f79c90c5ca42a2059c93e8e2e9 MD5sum: 6861952fd85b1efebfc95d6a120d9efb 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~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 180 Depends: libc6 (>= 2.4), libexpat1 (>= 1.95.8), libnifti1 (>> 1.1.0-2), 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~maverick.nd1_amd64.deb Size: 57604 SHA256: e4754cde9e0089b5774f5bbd3739171f5a68a0115cd50fd84a8c9aa1035fbfcb SHA1: 11983a5c86ae4fab0008551c4f17a5cc0d7ccb1e MD5sum: e7eacce11ea15f9b8b97ea6a242a36c0 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: libnifti-dev Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 624 Depends: libnifti2 (= 2.0.0-1~maverick.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~maverick.nd1_amd64.deb Size: 171296 SHA256: a10c25e55440328b1c4ac917735541ee5640176d41cc940c8c6564718d728bf6 SHA1: f0df2a274d65f9c6725a2769503bbbfbb2848624 MD5sum: fc52429aa09b32657fe7d09d3bc8ed11 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~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1896 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~maverick.nd1_all.deb Size: 245398 SHA256: ee170c249d6406abcf28bf82179b9327507e20bc341314191e9339ddbdf725f5 SHA1: 255d82f6d5115000ec47d28dd096cea340261cfc MD5sum: 6293a28d6b4ab6f3e08ee6f766fdd757 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~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 336 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~maverick.nd1_amd64.deb Size: 123028 SHA256: 6dc383c542f510dfe43d769101d51d117497011604bbe6dedd2dc2a2ea5e59e1 SHA1: 7e51b50bb5c2e73003a779dcb5818de3ded222d6 MD5sum: c00979d1767d6b98c166fea6528485b8 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~maverick.nd1 Architecture: amd64 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~maverick.nd1_amd64.deb Size: 43850 SHA256: c65760742eb06e6a12fd758b5db143564e27dd25b2eff3f98854e035566991f0 SHA1: 0175dc1e5a5701a201119d7901e256882549e439 MD5sum: 6c45ad3f49003ead9939df00c72abada 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~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 960 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), 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~maverick.nd1_amd64.deb Size: 252568 SHA256: 87d6e3f667c493b8615eadbf411b841fde7c47364991ee9256b7e13479a90175 SHA1: 13b3b2307374b46e0421791084bae02fed18238e MD5sum: 30c910d0f9bda2c9d2a4b47cc5812920 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd10.10+1) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: libdevel Filename: pool/main/libs/libsvm/libsvm-dev_3.0-1~nd10.10+1_amd64.deb Size: 28200 SHA256: 193d031c0667c81c1eab7c655db8aa82bc895d0f538ea78e9738faf2c5af9cb6 SHA1: a70f27e948c0c278ef67f0be99f8e76c5613dad6 MD5sum: 6d257eaec29bce115e3c9fe9775d3b16 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~nd10.10+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~nd10.10+1_all.deb Size: 2068 SHA256: 8c9fbe247ddc6f15bd3e3c64bfd7f1e3c69405f907da4fa7edeadfe05e9d21d8 SHA1: 8053cf3d05442da7571a056b726ac00a3d77b0e1 MD5sum: 14c1bd18250e02bbcd73cfc70bc8be5c 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 344 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~nd10.10+1_amd64.deb Size: 119876 SHA256: 24047b90f8ca21718b3c0e18b8ff35776f428a237e4339e9b737b6c71e5d0088 SHA1: ef009d9a0cc16aa59dd2d82f591307907a650964 MD5sum: 066e87449c23ca64e084440146e7f239 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 132 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~nd10.10+1_amd64.deb Size: 46226 SHA256: 578ce97fc2b26f01d354346e6d3d506ca96a57d8c2e3f32e9fe9b9633f1feadb SHA1: 50c1135ef4eda1297908a6a51f5f7ff9ac6fdc63 MD5sum: 7ebafe30e30b61295b29d5c8b2b6f576 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~nd10.10+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~nd10.10+1_all.deb Size: 60484 SHA256: 76811edf43b1aefb334ae653f274201c297bcccac8ac8530bec925b93dff1492 SHA1: 7e6ee936ff6b7679bd9f97fccede3700997e4f37 MD5sum: 6f1946dff229612af5767fcf8da60091 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: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 3940 Depends: libc6 (>= 2.7), libdcmtk1 (>= 3.5.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libnifti1 (>> 1.1.0-2), libqt3-mt (>= 3:3.3.8-b), libsm6, libstdc++6 (>= 4.4.0), libvia0, libx11-6, libxext6, zlib1g (>= 1:1.1.4), via-bin Recommends: dcmtk, lipsia-doc Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: science Filename: pool/main/l/lipsia/lipsia_1.6.0-4~maverick.nd1_amd64.deb Size: 1350080 SHA256: d6e5814498a44d6f9aa68c26dfe86e5a24398c6727efb40240ab872fad14e217 SHA1: 2887d9c808cbc0861958043d8dea9eee1ccc9c3a MD5sum: 109c0c9b499315d58dad5395c035eb9d Description: analysis suite for MRI and fMRI data Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This is a software package for the data processing and evaluation of functional magnetic resonance images. The analysis of fMRI data comprises various aspects including filtering, spatial transformation, statistical evaluation as well as segmentation and visualization. All these aspects are covered by LIPSIA. For the statistical evaluation, a number of well established and peer-reviewed algorithms were implemented in LIPSIA that allow an efficient and user-friendly processing of fMRI data sets. As the amount of data that must be handled is enormous, an important aspect in the development of LIPSIA was the efficiency of the software implementation. . LIPSIA operates exclusively on data in the VISTA data format. However, the package contains converters for medical image data in iBruker, ANALYZE and NIfTI format -- converting VISTA images into NIfTI files is also supported. Package: lipsia-doc Source: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 7004 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/l/lipsia/lipsia-doc_1.6.0-4~maverick.nd1_all.deb Size: 5539260 SHA256: 82338bf6b7642c81e3d25791b543695be0cfdce051003635d2635dd36680651e SHA1: 4605c3847915a816361fb32d35551861a05db84d MD5sum: 405aaaf745427fdfe430d25bb7e8b238 Description: documentation for LIPSIA Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This package provides the LIPSIA documentation in HTML format. Package: matlab-support-dev Source: matlab-support Version: 0.0.14~nd10.10+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~nd10.10+1_all.deb Size: 5474 SHA256: f7c51ef8d6cf009a787336f581c5ab43064c1bfcac0e2186ba42bcc981f84cb5 SHA1: 91e1e5645f489e771d0e8165293b163381ff044c MD5sum: 9ce86325f3bff1b1805906927bf5f508 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2152 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.11.0), libwxgtk2.8-0 (>= 2.8.11.0) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0-2~nd10.10+1_amd64.deb Size: 759042 SHA256: 94b8227d240903f5141f1b441b344f0ef78e4abb88a6b52ac80a286dc117d046 SHA1: 9c090c6b8d016862543bc13ef5a4933441c8e522 MD5sum: 07555bad9a440d9ffda8af0554ba3661 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: mricron Version: 0.20101102.1~dfsg.1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15628 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.29.3), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.21.6), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.8.0), libpango1.0-0 (>= 1.14.0), libx11-6, mricron-data Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20101102.1~dfsg.1-2~nd10.10+1_amd64.deb Size: 4866472 SHA256: b679734384c5ffa496ee802a5d038c78870c08b13ffb8da8cc1609e81c3afc7a SHA1: 73e8153baa56238033f9b9a15bfc7776658ca8f5 MD5sum: c5c15d414f18d1cbacdb736cac77eea9 Description: magnetic resonance image conversion, viewing and analysis This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20101102.1~dfsg.1-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1848 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20101102.1~dfsg.1-2~nd10.10+1_all.deb Size: 1663332 SHA256: 6e36b85e1aa8375a631d7d5dbaa98bd05b6455870758d6750bfeb22964533b15 SHA1: df0aa5780821ee74d1c85792d58c43fc9cb4c924 MD5sum: fdea6941e0150b7b3be0b6e6a57168e0 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20101102.1~dfsg.1-2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1216 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20101102.1~dfsg.1-2~nd10.10+1_all.deb Size: 734878 SHA256: de4bb2a18baf27afd32d6f244184419eb520127210c84907e01ed69184cabec9 SHA1: cb1d81fd766211b758bebefbad02701542de47c9 MD5sum: 1e5d2f1cd4fd641245dd360c9e917163 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.24~nd10.10+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~nd10.10+1_all.deb Size: 113474 SHA256: f534ea295967d9f8ff0999e9c521171d8d470b350f361931e32bc4aa402ac92f SHA1: 3e253ea3cf846f7900a3386c353871d10921620f MD5sum: 2b99306be72cd8d401b343410e541464 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~nd10.10+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~nd10.10+1_all.deb Size: 3794920 SHA256: 3e8f9de0af7320dc6806d92838875f5999f2cdbf0e2cecb6ff25deb2736db928 SHA1: 94eedc96ec14a891e9baeb258483188d4072f376 MD5sum: 60ce2f44323bede43baedb4a4dd2b377 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~nd10.10+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~nd10.10+1_all.deb Size: 12718 SHA256: ed53d5fabf52d16d4d35f1be28543a2fb0024ce5733a616ea53b76a19d1ff18c SHA1: b12ef72bc81b07057f0c987ae73c8deb00da872b MD5sum: b9c881ba2539c9d14e084a2f580d662b 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~nd10.10+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~nd10.10+1_all.deb Size: 5784 SHA256: 6ed87d1db49d1f027998984726f1b98a3594f0dc210d842bb51bc1104cf37017 SHA1: d57811cb4c47d7dc2cf77400dae513c7d516391b MD5sum: 9aea5a8afae669995f61faaeca22f40e 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~nd10.10+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~nd10.10+1_all.deb Size: 4958 SHA256: 81565f21d5894fe75302b48d0ea3494135213cb42edc9b325d938503fd38824d SHA1: 096a674b05b70e98c875ef2a79bd900faaaba0cd MD5sum: ecfe2f615393afe5cb6a34355dc61c9a 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~maverick.nd1 Architecture: amd64 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~maverick.nd1_amd64.deb Size: 62302 SHA256: e74b6f17efa9e03e859a450192fe35c0be66c33552d59123271c6704f169758a SHA1: b43f633e1de2af8b1463260231d9e49d819776ef MD5sum: 7947b46fb4d71466cde1e787e1b4f51a 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1608 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-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.7+20100313), libreadline6 (>= 6.0), 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~nd10.10+1_amd64.deb Size: 587074 SHA256: d04f886fe4842ecbda8745738572b6c781fe7134732c5d98d35af675007f1f60 SHA1: a65991ad148584de87c84541cb4d21ab8db54e7e MD5sum: 5707fa9dd7f8ee9ff35487c0ce3aa761 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: octave-gdf Source: libgdf Version: 0.1.1~svn62-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 384 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.4), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: science Filename: pool/main/libg/libgdf/octave-gdf_0.1.1~svn62-1~nd10.10+1_amd64.deb Size: 131368 SHA256: b11f0d7a3f48caed33a99a475cdbe8e8943e724e90a23756c9c5a8442ccb12dd SHA1: 753e8383a500a7738947b4d558b97a9e4795064e MD5sum: ef661721dc0c3d94b940bce052ffcbed Description: IO library for the GDF -- Octave interface GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides Octave bindings for libgdf. . HIGHLY EXPERIMENTAL -- USE AT YOUR OWN RISK. Package: octave-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2512 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.4), freeglut3, libasound2 (>> 1.0.22), libc6 (>= 2.7), libdc1394-22, libfreenect0.0, libgl1-mesa-glx | libgl1, libglew1.5 (>= 1.5.2), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.24), libpciaccess0 (>= 0.8.0+git20071002), libraw1394-11, libstdc++6 (>= 4.1.1), libusb-1.0-0 (>= 2:1.0.8), libx11-6, libxext6, libxml2 (>= 2.6.27), libxxf86vm1, psychtoolbox-3-common (= 3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1), psychtoolbox-3-lib (= 3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1) Provides: psychtoolbox, psychtoolbox-3 Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1_amd64.deb Size: 731904 SHA256: d85b3db5312b57918f80789c7b403e861a6f391bb7b58ffd5287a34b903959fe SHA1: e4ac130764c0e1287ea967eeafe29a7b9663b5ac MD5sum: 72f8a8165ebff0268b31bff54d90a7d5 Description: toolbox for vision research -- Octave bindings Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains bindings for Octave. . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), 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~maverick.nd1_amd64.deb Size: 163230 SHA256: fad9d47731fc75238a1a6da647637e110faa81579ba4ea54e5cd1d1e2423a911 SHA1: 724af280bad2953af20b8db0f737b6dde84908dc MD5sum: cde12d4b4d0cd81fb8f41175c72a78df 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2496 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~nd10.10+1_all.deb Size: 705284 SHA256: eb52e7e82e6a88e9fb8c8312bbfe5085b4f193c0510d8f64567cd0b42c287e2a SHA1: f135a17312a5b2c8c4537c5316d69310320fecfc MD5sum: ccff07cad0712f64bc81bfb1d5368023 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~nd10.10+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~nd10.10+1_all.deb Size: 2370856 SHA256: ae8fe94219d2f90a3be321a51283acaf03a87a7f280f9a65db3cc80781d07466 SHA1: 113e451f62071a7984057b8252dce997cb291b1c MD5sum: eb43aeef6e4a8220e78a5ec5dc152394 Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31372 Depends: neurodebian-popularity-contest Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1_all.deb Size: 13337664 SHA256: 307d6b90b3d1418a95b54d3d5c5cbc73bc02c5e4762a678dd406efebd059676c SHA1: f67720a46a2b6556bc5b21ad58837b3b2af18ab2 MD5sum: 507f9d585cde03334ec4528a6428d906 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: psychtoolbox-3-dbg Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2648 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1_amd64.deb Size: 779392 SHA256: b6abafd4563825d21376536988b45e9c0c1ffa205a230ee7101548582781be6f SHA1: dbbadbf9df2044d3981ece570d86aa9fd7b836d0 MD5sum: 0452f1529695fcd3ac09537b8dd01d92 Description: toolbox for vision research -- debug symbols for binaries Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . To ease debugging and troubleshooting this package contains debug symbols for Octave bindings and other binaries. . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: psychtoolbox-3-lib Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 268 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.1.1) Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.8+svn1934.dfsg1-1~pre2~nd10.10+1_amd64.deb Size: 59726 SHA256: 6d88b3697f599b7c485a04ba775491fdbc803e0971d49c90b44c6be2bec03366 SHA1: c4444297f4da9b65fe1af231895b08898f6ec928 MD5sum: 5cafee29319726658b1869392d06dd5b Description: toolbox for vision research -- arch-specific parts Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains additional binaries (tools/dynamic libraries) used by both Octave and Matlab frontends. . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: python-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1016 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~nd10.10+1_amd64.deb Size: 334928 SHA256: fc11a01328d12a2f57fababaa480a7178d65057c5aebfa2f37622b8bd32b48ed SHA1: 4182c59767f2ca23911934058a047ba23a60d582 MD5sum: 1d4a6fd88abe72393692299b001f74e5 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~nd10.10+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~nd10.10+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~nd10.10+1_all.deb Size: 313336 SHA256: b34df78da1e4974568015a96717c983f7f42f8025ffc8c317f95daba8875e9df SHA1: b849aa1a982a5ccf28c54a5a90d17f5de6a7aaf7 MD5sum: 0ebceec525779012f03b952d03dbb24e 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5436 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~nd10.10+1_all.deb Size: 1684074 SHA256: 2f1392bf7736f014c27749776f099a3a5f40fcbc1b10d2be2a64cbe116e310ef SHA1: 6da3e9b337f0bb98db39655ed25add84d1864a40 MD5sum: ce343b8e84ce408d2c72efffc4735162 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 216 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~nd10.10+1_amd64.deb Size: 54158 SHA256: 7009bd90b3c1e022eaef95b76f4f87796847c5f69b9f23e992b78d67ff93c608 SHA1: f67d9d8764ee28012160a4470582ed958fdfeed6 MD5sum: 16ce41bf60930028eb27c9ed6e68244c 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-dicom Source: pydicom Version: 0.9.4.1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1804 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.4.1-1~maverick.nd1_all.deb Size: 360124 SHA256: e11a5ca1d87ad166eefa550498743db961ae2727733b6f01d40d177c7d9c53dd SHA1: 3f767eb7480ef44c6c7a56a839f01e99f8cf849d MD5sum: 0adfc3a91a5b7419208ef94bb7a699a9 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.5.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2200 Depends: neurodebian-popularity-contest, python-numpy, python-scipy, python-dipy-lib (>= 0.5.0-1~nd10.10+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.5.0-1~nd10.10+1_all.deb Size: 1458532 SHA256: 3269c3e1a2d818ac4821c8135c95e9d63dd4ce3b798cb5b22b7ebd4224bf274b SHA1: f1735021cb33ff40680fb0614c98e349b24db91c MD5sum: 1471fa6743305dfe059c574440cf465a Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.6 Package: python-dipy-doc Source: dipy Version: 0.5.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3288 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.5.0-1~nd10.10+1_all.deb Size: 1949916 SHA256: 2e3fdf3acfc33d6eddb96146953d0941b28cd078975196e4cd886a1bd2df5d32 SHA1: 493083b35e31e39ce47f7ae341ad3af82229864d MD5sum: 554b462b27c9a5195ddb25af2f96ebe5 Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-dipy-lib Source: dipy Version: 0.5.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 652 Depends: neurodebian-popularity-contest, libc6 (>= 2.4) Provides: python2.6-dipy-lib Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.5.0-1~nd10.10+1_amd64.deb Size: 220800 SHA256: 2b34d7c4483f601fc939c37d6f3e928077a1a77c7b250260b906b217c8d4b8af SHA1: ff242b0849026cd3509499b7e66aa5486e1b7485 MD5sum: 12cd11e4ce955fcc83b942d050d5bd52 Description: toolbox for analysis of MR diffusion imaging data -- extensions Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Python-Version: 2.6 Package: python-freenect Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 164 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreenect0.0, libusb-1.0-0 (>= 2:1.0.8) Suggests: python-numpy, python-matplotlib, python-opencv Homepage: http://openkinect.org/ Priority: extra Section: python Filename: pool/main/libf/libfreenect/python-freenect_0.0.1+20101211+1-2~nd10.10+1_amd64.deb Size: 32190 SHA256: 87c42fedaa8e683aaa3cf666e93f80f3752965a1b00e274836f695188ac96ca7 SHA1: 000c01efcf7bf503df4da27155cb3d57d8a7ec06 MD5sum: 78c314539e8d28f60f8932932dc8c691 Description: library for accessing Kinect USB camera -- Python bindings libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package provides freenect extension to use libfreenect functionality from Python and includes some demo scripts. Package: python-joblib Source: joblib Version: 0.4.6-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 244 Depends: python, 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~nd10.10+1_all.deb Size: 38692 SHA256: a0cc8506c39110caa588b89a0da48a65a64bed86a2e661e0c17230882ba10a12 SHA1: 221ed9fbe866ad858176eb1c1ebbfd4a3a6fb3d8 MD5sum: bb0aa7c0fb35a2fe7f353ae0ad24e403 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd10.10+1), python, python-support (>= 0.90.0) Provides: 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~nd10.10+1_amd64.deb Size: 7610 SHA256: bd9663b6d36134dd9170ee1f6eee9c73170b51ea13ec3dbbfaee716c840533f6 SHA1: ab7dd46a0d835c0d7e9b35a26980c01a90d3c0e9 MD5sum: 616f9628b43baa9dbe32f893753e869b 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~nd10.10+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~nd10.10+1_all.deb Size: 450888 SHA256: 828a4c51c523aae5f546235ab4f2264d233758aaf611bbfff2c21b3cad28145a SHA1: 46b2ba002f80441221265334b62d72f41da6df64 MD5sum: 751c5acb88df90df1526327fcfbb6b52 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~maverick.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~maverick.nd1) Suggests: python-mvpa Provides: python2.6-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy_2.2.0~dfsg1-1~maverick.nd1_all.deb Size: 55840 SHA256: 8283e4ed0f79671d42b030290729eda5c540214d36125e1b06fd0b5b3c9a21c7 SHA1: 4bd22103a057fe6eecf855acf12e7e077d38fc19 MD5sum: b0171fc77f895c31946c6d7aed80768d 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: 2.6 Package: python-mlpy-doc Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1132 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~maverick.nd1_all.deb Size: 478862 SHA256: d4a1ca68b49b3b1fa7a35abdc1e263f00970d4a6cfde9fd5388508b5c3273ce9 SHA1: 3a203bde333a49868ea27467bb04fa9eb6870af6 MD5sum: da3c2ef1ca5363c2774440218a32fb73 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~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 312 Depends: libc6 (>= 2.3.4), libgsl0ldbl (>= 1.9), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: 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~maverick.nd1_amd64.deb Size: 70820 SHA256: bbfb06fd94be6d0d3288014ace6eece19424cb7b7c2fe7743673d8c6f9d9a568 SHA1: 01d9f84c07de1f9b4e987bf20d41c9df399364e9 MD5sum: 25a7200ed3517d83a23bc8e928a374c1 Description: low-level implementations and bindings for mlpy This is an add-on package for the mlpy providing compiled core functionality. Python-Version: 2.6 Package: python-mpi4py Source: mpi4py Version: 1.2.2-1~pre1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1156 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libopenmpi1.3, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0) 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~nd10.10+1_amd64.deb Size: 376202 SHA256: 8803845d8950c74d886f77f9c122a17ddb5ad2e90a62391ecaeb3370dec61ba6 SHA1: bbc3d3080caade391ab21f63d08414ddf34eb88a MD5sum: 8693100f20d29e0ee4ccb8417d76a087 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2064 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.2.2-1~pre1~nd10.10+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~nd10.10+1_amd64.deb Size: 563238 SHA256: 2a0f207f06dbfedc4aa1cde0ebbe36a6e7a0b6c1be01d69ce138b14e90266253 SHA1: ff8bf7e4cf55617a94bbfc8589fd5be250ee0f15 MD5sum: b43ff55aa584174ef00782c1c7e18bdb 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~nd10.10+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~nd10.10+1_all.deb Size: 54814 SHA256: 1bcb050af5a49af61314a1053fb07ff943c6267a9ca673e7bf3a391006844725 SHA1: 9aca99120fe1caf7b242bf68424c109eb12ba074 MD5sum: 6539799e7d5bce2eaf1ebfdd041f3e53 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4064 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.7-1~nd10.10+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.7-1~nd10.10+1_all.deb Size: 2188296 SHA256: b39f6844e01f382e5607cbc8213d6d135f086dbb66dac681ffd90a5356770ad5 SHA1: 45249d7868984c7efceeb7a0fedb8184da294d02 MD5sum: 73eec39f311e6c0fe0fb575fc85567e3 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: 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.7-1~nd10.10+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~nd10.10+1_all.deb Size: 8656092 SHA256: 0cf12c5b6e5d81595f42822b7a7101cf72b6f78ba111e13aa4f6f76e83012ae6 SHA1: b24f2cb2866da7ba0108738c4966c1badacaf676 MD5sum: a44463cd30754c540d7746186d959df3 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: 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~nd10.10+1_amd64.deb Size: 34988 SHA256: e0ecfc64b42c7372b908b502a608e15779ebb88a6824699e7fab31f3e79cd7e5 SHA1: 0c2cc6cb40be04dea0c7107dfd04f977ea6fea16 MD5sum: 18f29d4d2817330a9fcd0869677f4956 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: 2.6 Package: python-mvpa-snapshot Source: pymvpa-snapshot Version: 0.6.0~rc2-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4644 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-numpy, python-mvpa-snapshot-lib (>= 0.6.0~rc2-1~nd10.10+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py Suggests: fslview, fsl, python-nose, python-lxml, python-scikits-openopt, python-rpy, python-mvpa-doc Conflicts: python-mvpa Provides: python2.6-mvpa-snapshot Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot_0.6.0~rc2-1~nd10.10+1_all.deb Size: 2286962 SHA256: 1b3202ed11b7b8073ba6a68b8271ea1b6b19880a91b246c2f4f157df5e85db6d SHA1: 26e5e3a6a15bc65e35f9f5ccc69c12fcece92f08 MD5sum: 37492b3522c0935299d593cae29c2682 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: 2.6 Package: python-mvpa-snapshot-lib Source: pymvpa-snapshot Version: 0.6.0~rc2-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 172 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Conflicts: python-mvpa-lib Provides: python2.6-mvpa-snapshot-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot-lib_0.6.0~rc2-1~nd10.10+1_amd64.deb Size: 33106 SHA256: f50567e7e79f7f26fb623528ad5a4216fb356a04ec5ee4e0182805d796c6db6e SHA1: 666429f966b2da2bf83cacc2eedee18026236eec MD5sum: 1a79faf57ba013ef9d65e7501aa2f316 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: 2.6 Package: python-networkx Version: 1.1-2~maverick.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~maverick.nd1_all.deb Size: 679658 SHA256: 1b57041f1c6383b2c67aee7c5adced6e17c27769de5768b2a5625146b12aefed SHA1: 7dea67b649381618bd855eeea17892721e71ec39 MD5sum: ffbdff4561f896997795d265f14d2263 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 Source: nibabel Version: 1.0.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2776 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom Suggests: python-nibabel-doc Provides: python2.6-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.0.0-1~nd10.10+1_all.deb Size: 1056526 SHA256: 3b11c09b2c115daf761f1963f87bfdb5c5eb28c5cec63f91d437c1bcbd9a1c1a SHA1: 4b99d8369f4e768f954b8875fba2ece9971dacd6 MD5sum: 8d566a6c283d548c35ba2b09728d2d86 Description: Python bindings to various neuroimaging data formats This package provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.6 Package: python-nibabel-doc Source: nibabel Version: 1.0.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2716 Depends: libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.0.0-1~nd10.10+1_all.deb Size: 398618 SHA256: d66b8ac32caaeaad1ab1314ba94df2f0cb7d1cf1396f95916534790f0bb932c5 SHA1: d965fd80013ee6b53bbb48fef2e7f59cc53b415f MD5sum: b66cadb2152267e718c84f17a3af396e Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nifti Source: pynifti Version: 0.20100607.1-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1132 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libnifti2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, python-numpy, libjs-jquery Provides: python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-2~nd10.10+1_amd64.deb Size: 290422 SHA256: 69839ff36810c93da924bf87ab1282a52fb57428e9e6a10622cf7c9f064a62a9 SHA1: e3185755a89db67181f7f710d4fb0a745fdfbf1f MD5sum: 5eefbe3377e569a5e58243c48eff5572 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: 2.6 Package: python-nipy Source: nipy Version: 0.1.2+20110114-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4588 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-numpy (>= 1.2), python-nifti (>> 0.20090302), python-nipy-lib (>= 0.1.2+20110114-1~nd10.10+1) Recommends: python-matplotlib, mayavi2 Suggests: python-mvpa Provides: python2.6-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.1.2+20110114-1~nd10.10+1_all.deb Size: 1165262 SHA256: 3ac3171cae5e2499d80db10ddfa2dc31329b252f1a7e1c9678eca29e0d8ba8cc SHA1: 543561f16f2afd613e04ae5dc3476d826d88c2d7 MD5sum: 0194bab063b42921ce5cd2df93cb3d05 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: 2.6 Package: python-nipy-doc Source: nipy Version: 0.1.2+20110114-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11296 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~nd10.10+1_all.deb Size: 2801620 SHA256: 1509968b3dba233ea4afeb89619e94ff27d25a33fe7665cc9b9c6f414555f8a9 SHA1: a0abeb081f1ef3befb49975f77d4285b886205d7 MD5sum: f73ca8255af7735db60c914e89c2ab7b 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2824 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.4), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0) Provides: 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~nd10.10+1_amd64.deb Size: 870746 SHA256: b5cc953c014d69c2edfac40b4b6cb483e513522bd5f6f0cd8fae0bcfd7627078 SHA1: 550a3b54ee3f8ace0ec8be7cd510d430a23f45ef MD5sum: 5d65a265c10bf13808814c17c1be91c4 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: 2.6 Package: python-nipy-lib-dbg Source: nipy Version: 0.1.2+20110114-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2940 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.4), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-nipy-lib (= 0.1.2+20110114-1~nd10.10+1) Provides: python2.6-nipy-lib-dbg Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg_0.1.2+20110114-1~nd10.10+1_amd64.deb Size: 933638 SHA256: 32bbaf5b85b1cd709825bc78fe36084b7e420013cd32407f10dfd56e367afe79 SHA1: 1e0ef4be99fa19dcf40400df22d839d50df8a692 MD5sum: 02ba6517fa5aec2ed16ab1092acfa2fe 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 debugging symbols for architecture-dependent builds of the libraries. Python-Version: 2.6 Package: python-nipype Source: nipype Version: 0.3.4-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1816 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits, python-nibabel, python-networkx Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.3.4-1~nd10.10+1_all.deb Size: 316082 SHA256: 188c5a6a71ae09510f4bc48473cca961cd146aabf14925b1093f658621aeb285 SHA1: ebbeb68986cbe9f2882c3a5fe9829dc80bdd42ac MD5sum: 47910ffd13236806297fc30c64617d6e 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: 0.3.4-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3940 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.3.4-1~nd10.10+1_all.deb Size: 882262 SHA256: a062a81f5d18db97cdf8574769b550f3b8aa7824be6c671603d77821f8bcde66 SHA1: 8cefd5a6c349eda3862b85cfc0da472c3f61edc8 MD5sum: e6413bda9e3707ba9f1e1608b2fd0574 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.2-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 752 Depends: python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.2-1~nd10.10+1_all.deb Size: 312900 SHA256: a597d5285ed2764312a046981666d2fa05fe4e643eecaa260b7c0aec0eb27449 SHA1: 3396ad0f1d125996d6abd2bdc9840d03fe9733ec MD5sum: f121a3106a95ecadd3bad854468df318 Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.2-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4096 Depends: libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.2-1~nd10.10+1_all.deb Size: 2663834 SHA256: ad266363c0d0170244078baec756d2712034342903a5a6546ea73d52200fcea9 SHA1: b308c4bd273ba626d076fe7e83544668773ab7d9 MD5sum: 1226038c73f2b8fbeabd32bf60f09ad7 Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openmeeg Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), 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~maverick.nd1_amd64.deb Size: 157526 SHA256: 3a95190b1f9aa9128f1ff07a9927bf4285f605c029ff87b7f763905047efe436 SHA1: 303aa983399d068350fe8c0b289533ecfd0ee69e MD5sum: 59f8f5cd00801d0d43f458256051b35c 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~nd10.10+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~nd10.10+1_all.deb Size: 57464 SHA256: dc9ef21446d6be3eaa28830f284a35d1c6d002cfe2585c852ea02a623658dd96 SHA1: 30d1cff8bcd72a2c020cfd141e0cc079b17ac5c5 MD5sum: c14cd6db86450b62ee4c87913a7bcd6b 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~maverick.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1696 Depends: python (<< 2.7), python (>= 2.6), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~maverick.nd1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>> 1.0.22), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0, libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.6-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0-3~maverick.nd1_amd64.deb Size: 381566 SHA256: 5c2db2b03d580c1c40aeb4768b772aae61ebab32a1564b46d58eab61efb2e4a1 SHA1: f900b6fb4e1cabbc59cf0d16db41753165d0422c MD5sum: 8f8968da5e76f1f8748211134d2e645a 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: 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~maverick.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~maverick.nd1_all.deb Size: 817816 SHA256: c48e4f818f9d5f08f12d59d6c1131b7f163f29c8801d3373889a7c20795d2d9f SHA1: 82228fe2202df811391f45df27ceaabcc7abca89 MD5sum: f4abbefb9d51c38b9370f48229577970 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~nd10.10+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: python2.6-pyglet Homepage: http://www.pyglet.org Priority: optional Section: python Filename: pool/main/p/pyglet/python-pyglet_1.1.4.dfsg-1~nd10.10+1_all.deb Size: 972184 SHA256: 48cd04fec2110ce036d16fc32550296c817eef64f138a48b54456bb177ade3d0 SHA1: fe964cc057d4573c9e10663f8a9e2c78b4b69a80 MD5sum: 95d34f27e08151c595ef5e023ab56fe9 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1004 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~nd10.10+1_all.deb Size: 171194 SHA256: 6baf0a7df0477e569f6a4335a861ed6b4c2fcf5aa990b8f8ba3eba2183911d67 SHA1: 6ea818377c0d35c2f5612fa79b98f818b83c5c9e MD5sum: 593f269af43bae4f52e44df45db94407 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-scikits-learn Source: scikit-learn Version: 0.7.1.dfsg-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1236 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-scikits-learn-lib (>= 0.7.1.dfsg-1~nd10.10+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-scikits-learn-doc Provides: python2.6-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~nd10.10+1_all.deb Size: 270416 SHA256: 9c039e4e6ff140bab5fa94d1ad678ac375e4379fcd7b73097c92280f0935129f SHA1: 2a8718f9cfc92b46969ca966733e3d10aaf28872 MD5sum: 4eae97977d5f5991cb180a6f50bcafa1 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.6 Package: python-scikits-learn-doc Source: scikit-learn Version: 0.7.1.dfsg-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9048 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~nd10.10+1_all.deb Size: 4457288 SHA256: 6fefa88837ee33553ffad471770d8eb4c69af188293160e95b79ed5c99cff5cf SHA1: c655479ba29a2726b96e2ca842fcad22a451e9c4 MD5sum: 37dc18aa1b243bd84a6cac12a232c752 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1228 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: 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~nd10.10+1_amd64.deb Size: 468144 SHA256: cb358eaf3f130f587186758eccd5c4c3fa84aec82d61f7320e4b8bf8e15b652c SHA1: 37705d218cf831eee2c8b5c61b1ecf6f6a664abc MD5sum: 2e7b2795f655079be9c900aad591ec12 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: 2.6 Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd10.10+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~nd10.10+1_all.deb Size: 1260220 SHA256: f28f090879ac7000b7776776b7abf460bd1fa769febf27683218710014d1ea0f SHA1: 617ccda8c98c0be4c7d6ab50a61699a10c19d219 MD5sum: 6927c8799a2a3a86523898580e080d10 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-stfio Source: stimfit Version: 0.10.12-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 488 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python-numpy Recommends: python-matplotlib, python-scipy Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.10.12-2~nd10.10+1_amd64.deb Size: 184476 SHA256: 91862393730501582b0b301d3054f553a6535d710fae730bb60711062b4dc09b SHA1: c00a3e2957329ac46a793521460920ece8761ad5 MD5sum: 8404441d1ffbaf0512c476a321c9b329 Description: A Python module to read common electrophysiology file formats. The stfio module allows you to read common electrophysiology file formats from Python. Axon binaries (abf), Axon text (atf), HEKA (dat), CFS (dat/cfs), Axograph (axgd/axgx) are currently supported. Package: python-sympy Source: sympy Version: 0.6.7-1.1~nd10.10+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~nd10.10+1_all.deb Size: 1696290 SHA256: 93ac728b1ec8442b324d0a353c6fc7606cc1b2623934399301b8e1626c5497c1 SHA1: 561bfc91c596b02c7212a563936557dea2e4fb1b MD5sum: 675f1e681ed76bd6478e9e9f2d7b3a24 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: sigviewer Version: 0.5.0-1~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1116 Depends: neurodebian-popularity-contest, libbiosig0, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.5.0-1~nd10.10+1_amd64.deb Size: 455810 SHA256: 9a6d3ff082d9bfe67977eec10ac5f924aa7daab040f4e2778c881b68d7dd1e29 SHA1: 1f63deb262d22836684fe968442250b7a6d425ed MD5sum: fd1a9156b23646d6c34380a0009f06a3 Description: GUI viewer for biosignals such as EEG, EMG, and ECG SigViewer is a viewing and scoring software for biomedical signal data. It relies on biosig4c++ library which supports a number of 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 . . Besides displaying biosignals, SigViewer supports creating annotations to select artifacts or specific events. Package: spm8-common Source: spm8 Version: 8.4010~dfsg.1-4~nd10.10+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~nd10.10+1_all.deb Size: 10087164 SHA256: 8007bc62ba7006afd6451bd05fff4e2581b2621c4519241f7bb2db1e1b9b2083 SHA1: 7e6f60a193d9426d17f4047139da49ab092603b6 MD5sum: 0064afec0e829b7f1440809b3853221b 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~nd10.10+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~nd10.10+1_all.deb Size: 52168476 SHA256: 7bac5683eb59b7206b5c4821c5cffb5bce0339df9036be41b45ef22713fb296b SHA1: e2f82eab7732202daa905e86ae638f9b1c8a48cc MD5sum: 1added7e13cd8670d357a073e609ecdb 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~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11288 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4010~dfsg.1-4~nd10.10+1_all.deb Size: 10423808 SHA256: ca11d910915cd84484a8c70d142d3b26050b648a32a35753177955bfec68f222 SHA1: 8ee172510cc84f36f7564ab9ede457383340943b MD5sum: 87d85d8b05b8f8bb9b27c219db5e8b4f 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: stimfit Version: 0.10.12-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2072 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.11.0), libwxgtk2.8-0 (>= 2.8.11.0), python-wxgtk2.8 (>= 2.8.9), python-numpy, python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.10.12-2~nd10.10+1_amd64.deb Size: 714664 SHA256: 0d1c1d701355bbe9305a5d4f072b652f14da6be70879f17751432c75b24a6b7e SHA1: 2406a72818ce237c96a6e05e2fee04c0a7880a93 MD5sum: 8359d39969a15ed1dc4e6de088129128 Description: A program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.10.12-2~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 14824 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.10.12-2~nd10.10+1_amd64.deb Size: 4955520 SHA256: c130039e67473806fc867374d9a644f024cb51a7d47be0a69a7a16adcf826a09 SHA1: 09ceb6de6f96a57953848cda637a5194345f90fa MD5sum: f7b70947734a4c9bf2baa72146461274 Description: Debug symbols for stimfit Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. 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~nd10.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10132 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4:4.5.3), libqt4-qt3support (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), 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~nd10.10+1_amd64.deb Size: 3708612 SHA256: dec56bf3009e7939a9c1bdfef2e49a7ba2899718a6bc79de63d8989c030aa1dd SHA1: e3e555a3f2b487d958e8bf16c1d63a35b43ebfc8 MD5sum: d0025377c53ae195f0514a76f388b2d2 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.