Package: condor-doc Source: condor Version: 7.8.8~dfsg.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6121 Depends: neurodebian-popularity-contest Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: doc Filename: pool/main/c/condor/condor-doc_7.8.8~dfsg.1-2~nd70+1_all.deb Size: 1466672 SHA256: 6a970eb963f109b4d91cf0f66127c5e8d6a8980fd83be5137231c3f752131a01 SHA1: 1459bdb70d907cb0e342437ad9029ba414c68309 MD5sum: 1a273d8944188d36b9eb029619118f9b Description: distributed workload management system - documentation Like other full-featured batch systems, Condor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to Condor; Condor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, Condor can also effectively harness wasted CPU power from otherwise idle desktop workstations. Condor does not require a shared file system across machines - if no shared file system is available, Condor can transfer the job's data files on behalf of the user. . This package provides Condor's documentation in HTML and PDF format, as well as configuration and other examples. Package: connectomeviewer Version: 2.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1578 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2 (>= 4.0.0), ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.1.0-1~nd70+1_all.deb Size: 1356156 SHA256: 84e3a8e4487cd67005eaf2c292b248e7e812057408ca7b7e012d71c3684298c2 SHA1: a20067603c1694d3c598d7e261e2bb64a98253df MD5sum: 4325ba9177d6224461c4520b1b7a41a0 Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: coop-computing-tools-doc Source: cctools Version: 3.4.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2319 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.2-1~nd70+1_all.deb Size: 310890 SHA256: ca1fc4a117105875244c5c1a16994aa4e1c7496de9d177e96bbd351def1da0b5 SHA1: 154b372d4c5b7a25d5885e2ae8d79e64808671b2 MD5sum: c5f2ca94795a12217de0438befa22e8d Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd70+1_all.deb Size: 7224720 SHA256: a25c47daa7e5cabbab1e2864994d7ca0d5b207e5609c31fe0f62c32fae733590 SHA1: 6a5b78425b50d335c0f1e49bc20cd68aae0ab3fc MD5sum: fdcfc99b0c53436258c20f5eee125e50 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: fail2ban Version: 0.8.10-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 402 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.10-1~nd70+1_all.deb Size: 134644 SHA256: b270e6428fce15c999820a5070dee2bb378bda6e692c4f5bb77aba84e93249ac SHA1: 2291b9564907b925c69f87cb11cfa1e62dbfd1ab MD5sum: 6aa69374223086e9846c9795317252a2 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Package: freeipmi Version: 1.1.5-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common, freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.1.5-3~nd70+1_all.deb Size: 932 SHA256: d052644b2bb73930387779f71d65b14b041c033fb7624b1622b3a5b8f7697f8c SHA1: 09e5ab7649c67b3f40276828fb0642fcdf5eb6ba MD5sum: a5c30bc228c954949bd8be2e8beff792 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This meta-package depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 380 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.1.5-3~nd70+1_all.deb Size: 296952 SHA256: 8065c241cbdd833fab835d8944d5dca4c060573142745b81652ac0ac9000adfd SHA1: c508dc59367a85942fa9b41e3a58587e7c307c5c MD5sum: f78d625118a54dc59c26b3a89c6db926 Description: GNU implementation of the IPMI protocol - common files FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package provides configuration used by the rest of FreeIPMI framework and generic documentation to orient the user. Package: fslview-doc Source: fslview Version: 4.0.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd70+1_all.deb Size: 2346532 SHA256: 485b06c824c12100413729f1a795e7963897b84bf9b92e2b8b91b5f207f1e709 SHA1: fe14f20c5faf550a29cb9b24121fc2cfddedacd1 MD5sum: 641476378b45b8e334faefe1ccdff8cb Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gmsl Version: 1.1.3-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.3-2~nd70+1_all.deb Size: 16312 SHA256: 81fa733b729f71a9b6598c572347e092fb1daba4b7a98ed5cc765aee3ee44a2f SHA1: eafa70ea9e88be816f77e1f4d7c3a0bedcb5476e MD5sum: b8fe56193108337856824bed0406a597 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 302 Depends: neurodebian-popularity-contest, guacd (>= 0.6), guacd (<< 0.7) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.6.0-1~nd70+1_all.deb Size: 277650 SHA256: a50628f5b19e7baa2256bf86499957f7c20fb920c2f9b66d51605d3dd24f7015 SHA1: 20433956b4e76f6b1592da48efe86be68ec817f9 MD5sum: 8eff61e47ae8dd6ba2a80951ef283625 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.6.0-1~nd70+1_all.deb Size: 5176 SHA256: 48da6fb47245d1fc57bd3f142db9cdb418b00bfd5974d948917e14faaa13a756 SHA1: 4810336460c661e65fdc5801a3358efd56603434 MD5sum: c844736c4586d9a1af28d3f58ba0a8dd Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: impressive Version: 0.10.3+svn61-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 306 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2), perl Recommends: pdftk Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.3+svn61-1~nd70+1_all.deb Size: 155578 SHA256: 3546e182db03da8186d7c6afe785210102091aed423968250e260dc1f075f12e SHA1: 6467c36ac47b1993c3dc487a943ef0dfc45beedf MD5sum: 0600a7c30074b40da9a0d9207f1d6d04 Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd70+1_all.deb Size: 9652 SHA256: fac3ad8fc2cf1126a2b7fd3a9497594c3372cf7ae5a006d552d0b18e97334a11 SHA1: 803b8e967a16602928187f76ba0a8813d6a68866 MD5sum: c70545ff21713e721dbd16f9a195cbde Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.2.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2677 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.2.1-2~nd70+1_all.deb Size: 2408076 SHA256: 29e82b1719fa11a33cda9faa400bc033c0ee8241c9d9917e01f6d959ad5ce7e9 SHA1: 15ff635e7fb34b9052290258ec8b376a11d06f0c MD5sum: 8e4aa23ac9731dec02e834515eb4d260 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4808 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd70+1_all.deb Size: 1306320 SHA256: d259e419c42ab2f29c62a358f1b70ac483246c60043a213cf2a0e2ebb27940b9 SHA1: f1da0836b718381b16709910018994a049da53cd MD5sum: 445c27ebd25688a209351c5432f11a9b Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16664 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd70+1_all.deb Size: 7243134 SHA256: a34015da70830de42c97645c790f2fdc179da0b1b48848617dd8926b23b017e2 SHA1: 50455b67f63f0e2b7b95c4cda4c6f61feb14fa09 MD5sum: 4c412f1cfd211f9b4a81a0f7986b445f Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd70+1_all.deb Size: 896 SHA256: e6bf753904ea6c85c72689ffbe60b4f7b77243e38733c4c8a486c9b6fdeb69cd SHA1: 9226720c79cf6b2fecae5206e4a5af313318d950 MD5sum: 587920ae0a922c5a9ea5d60f75c52367 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd70+1_all.deb Size: 824 SHA256: 0097d83205fc332bebc5e9e178063ab3c6d740909a6c8ce7da2930d300556864 SHA1: ced489b459fa0edfd0a0414d2a0b4cac6cd7e9a8 MD5sum: 172195f46a65a28d182025cd62cd2503 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd70+1_all.deb Size: 910 SHA256: 009e2f9b28f70112713dfd1fa64bff7958a250fc2d5f622ef925c49d15afa5a1 SHA1: fb211e7d7981402a4329181ed727148ee38195d4 MD5sum: 9bb764488392203162c98cee5d3f794d Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: libeigen3-doc Source: eigen3 Version: 3.0.1-1.1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10344 Depends: neurodebian-popularity-contest, ttf-freefont, libjs-jquery Suggests: libeigen3-dev Homepage: http://eigen.tuxfamily.org Priority: extra Section: doc Filename: pool/main/e/eigen3/libeigen3-doc_3.0.1-1.1~nd70+1_all.deb Size: 2377384 SHA256: a49fd82e5f6a6d048154bd60d83245d840e38ec31ca1c90607c04479eaf6f04a SHA1: 551f098e9a8eae57dc8ac6baceb92ff5c87871e9 MD5sum: aefa7c3d5f3f5bfd5e3a481d932d7477 Description: eigen3 API docmentation Eigen 3 is a lightweight C++ template library for vector and matrix math, a.k.a. linear algebra. . This package provides the complete eigen3 API documentation in HTML format. Package: libfreenect-doc Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 482 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.1.2+dfsg-6~nd70+1_all.deb Size: 90828 SHA256: 23e2aa088d04437e5bb29a125098e50c02306c00bea9473ecfb05fb103d8a81b SHA1: 5d52d5550dc4b2558540c4cfa22479fd6e92c908 MD5sum: 36d9e1dd9960866bef703a872163d9a0 Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libisis-core-dev Source: isis Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libisis-core0 (>= 0.4.7-1~nd70+1), libisis-core0 (<< 0.4.7-1~nd70+1.1~) Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-core-dev_0.4.7-1~nd70+1_all.deb Size: 68948 SHA256: 71ba81e336312edd85331e45ad6c689d1133fe332506a79eb1d4e41946534675 SHA1: 7761d9efa1a6a2cadc67a0f2e546b165f088f855 MD5sum: cc18de68a3f8d8942ad55d38751a2d01 Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides headers and library to develop applications with ISIS. Package: libisis-qt4-dev Source: isis Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Depends: neurodebian-popularity-contest, libisis-qt4-0 (>= 0.4.7-1~nd70+1), libisis-qt4-0 (<< 0.4.7-1~nd70+1.1~), libqt4-dev Conflicts: isis-qt4-dev Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-qt4-dev_0.4.7-1~nd70+1_all.deb Size: 5992 SHA256: f848c976204b1b3090c9bcba159204365ee5620986f0cadd15bc6a6b8a9dde80 SHA1: a9cc9f1a3bd89a7545ffe60b6ccc874c874986a6 MD5sum: 96ef7f5956383a9fe46cea8c8843d7cd Description: Qt4 bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libopenwalnut1-doc Source: openwalnut Version: 1.3.1+hg5849-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39512 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.3.1+hg5849-1~nd70+1_all.deb Size: 4548600 SHA256: 90203b0a3ac595cb4a23609f394cbff8151a65200e80d213efe2dd0d5aa857a6 SHA1: 0d07fa4dbbb13da94af06d1415a4f1de03cf0296 MD5sum: 5491afc50fca47708ff44779e9d56238 Description: Developer documentation for the OpenWalnut visualization framework OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: libvia-doc Source: via Version: 2.0.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 903 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd70+1_all.deb Size: 118466 SHA256: c508ad5f2de2d726a6ec321a5dda11ae53d8d1991ad9d407c85cfd9190a25184 SHA1: 20c0141728ccf9539a2a460c758d63970ddd85a2 MD5sum: 7094bbe0e4041f7c7ad8b07781132693 Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: matlab-support-dev Source: matlab-support Version: 0.0.19~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.19~nd70+1_all.deb Size: 7222 SHA256: c2f02411c154a5e7ee649826684229a6039ca11e4202895d685b17c583367a0f SHA1: bf2bb4f466a020900a5b75d882a5daaacc6cc8ce MD5sum: 27847c10f995a1766934bf85ef8602ac Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mricron-data Source: mricron Version: 0.20120505.1~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1681 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20120505.1~dfsg.1-1~nd70+1_all.deb Size: 1666896 SHA256: 452a32a313173f9280306a97d3c4cbebc31e488a966f740955d2a828ad63a839 SHA1: 8d3c10732e9f7eccedb844c369010c4cfae76feb MD5sum: aa879d59797cd6348b69c43277fa61a4 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20120505.1~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 982 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20120505.1~dfsg.1-1~nd70+1_all.deb Size: 738386 SHA256: 05857a774647cba8155dcf527dc5f256cdccf239b2e82675af5250acfebd1359 SHA1: 07b2f3b7ebdc74e4a91d8caa61ab08d2a1cd3807 MD5sum: fa0eb7fa2927fd6577e0a6ae97e02ec8 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mrtrix-doc Source: mrtrix Version: 0.2.10-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3491 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.10-1~nd70+1_all.deb Size: 3322016 SHA256: c2268744669b0b2143112d1a1461fb4a5b99e9c102a4852e65f0d83a711ae63a SHA1: 3cdd127233e21960d2567367934029df0a69d2ec MD5sum: 8ff9467f45ceac4eb7425623f6545507 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 142 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.31~nd70+1_all.deb Size: 115314 SHA256: b9170c9731932c00415af37d1751069de4f1398eb9116f072cbd46501a0666a2 SHA1: 0cc0094d3964d8ba2cf3cf5a6c9b596b3cbbfb4e MD5sum: ee8e230a143dc4e63b804a0fec575dd9 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5762 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.31~nd70+1_all.deb Size: 5351202 SHA256: cad43d5482d3da721bb836d7630fc36ac35b02e51990ad80fe176babdca00574 SHA1: a90cc8a7bac8c790ae943288ee7e27dd4f4aa7a0 MD5sum: 59a4723a7715ad5d742668a9e4cc2bb7 Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm | lightdm, zenity Recommends: chromium-browser, update-manager-gnome, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.31~nd70+1_all.deb Size: 15192 SHA256: d7957826c7b08285bdef6d5ba43a68bb594fad9fbc71807fbf492a4338efcc38 SHA1: bc6aae33a26c40297523ae2da57fa2fe13327420 MD5sum: e9a936b8d260998e7c3f28813f4d6076 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.31~nd70+1_all.deb Size: 7486 SHA256: d7277f549050c006cd29e5ed2abe115b02cc45e9ac1432a123a5a00f96476249 SHA1: 56e60f56cd424d1f25df3e14eaf37fad4c844e4b MD5sum: 7db731e36f5df6e96a98b41e39097726 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.31~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.31~nd70+1_all.deb Size: 6704 SHA256: 3a0d218aa948766a64bcbb5f891a3cd1ff6c459aa1e346322f90fe7e79e2b6c5 SHA1: e5acd691f9ecbac33d1b6535ab8d41be6ebfa7af MD5sum: 9cf4308caf02c86321f48ba1f6f69c40 Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.6-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.6-1~nd70+1_all.deb Size: 615134 SHA256: 83719a424ba0638d067e9e162a8f87bd11de564b1e90421652f15fe7d6bb774c SHA1: 711c9199c005d2a098f95c849f81c6d598abdc84 MD5sum: 90f5a644a728c59535cea93751f104ba Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. Package: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 36 Depends: python-nibabel (>= 1.0.0), python-nipy (>= 0.1.2+20110114), python-dipy (>= 0.5.0), python-nipype (>= 0.3.3), python-nitime (>= 0.2) Suggests: python-mvpa, psychopy Homepage: http://www.nipy.org Priority: extra Section: python Filename: pool/main/n/nipy-suite/nipy-suite_0.1.0-2_all.deb Size: 3898 SHA256: 882c8580ebd2d458a92f8d851d1ec9291fecf05f6ed98a8b754eb831c95368c8 SHA1: 6501d1d201160520f5aad29d0f9007c17b7d9778 MD5sum: eb090e568264d2f439892bcb98485b8c Description: Neuroimaging in Python NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. nipy-suite is a metapackage depending on the projects developed under NiPy project umbrella, such as - nibabel: bindings to various neuroimaging data formats - nipy: analysis of structural and functional neuroimaging data - nitime: timeseries analysis - dipy: analysis of MR diffusion imaging data - nipype: pipelines and worfklows Package: nipy-suite-doc Source: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 32 Depends: python-nibabel-doc (>= 1.0.0), python-nipy-doc (>= 0.1.2+20110114), python-dipy-doc (>= 0.5.0), python-nipype-doc (>= 0.3.3), python-nitime-doc (>= 0.2) Suggests: python-mvpa-doc Homepage: http://www.nipy.org Priority: extra Section: doc Filename: pool/main/n/nipy-suite/nipy-suite-doc_0.1.0-2_all.deb Size: 2250 SHA256: 54985bd9d6eaa352608b357f2deeb066bd2ac12d3c2e463082f5d9178701bbad SHA1: 5d2f5e94ff6b7ff737fe966f4a2e5ff67df93cca MD5sum: 37d2f8b6b6d203edf208afb0cdb56fa3 Description: Neuroimaging in Python -- documentation NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. . nipy-suite-doc is a metapackage depending on the documentation packages for NiPy projects. Package: nuitka Version: 0.4.4.1+ds-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1755 Depends: neurodebian-popularity-contest, g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python (>= 2.6.6-7~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6) Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.4.4.1+ds-1~nd70+1_all.deb Size: 425912 SHA256: 5e8e5f7785edae62ebb6f3f0c6c6c134ec0cf38cf7474e39ff6337977c6df902 SHA1: 99760b5965b3adc0f7b6bf7d2fb50bc35c416c30 MD5sum: 8f4a80655729dc095ce0bf925bbaf408 Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: opensesame Version: 0.27.2-4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25510 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.2-4~nd70+1_all.deb Size: 24409894 SHA256: d49d05bccbfedbe85a7bc26663be2c41238c7b58bfee3958a254b609e73a9751 SHA1: 4ee3efe2212801eaa6838a4964c473837d784486 MD5sum: f0169dcdda018cab1ebb41ae3611d202 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Package: openvibe-data Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9328 Depends: neurodebian-popularity-contest Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-data_0.14.3+dfsg2-1~nd70+1_all.deb Size: 2024456 SHA256: 7b72cf2a61f9764f3d6d4b8c632db691ffb517dcdb6d500c521b8a1eec381302 SHA1: 107a4c5c7588594034039a389571a77eb3914d1d MD5sum: b10cbfaf7110dfa2a5582c30cbe29212 Description: Software platform for BCI (Data files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the data files. Package: packaging-tutorial Version: 0.8~nd0 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0_all.deb Size: 1488332 SHA256: 491bc5917f698fee06888998e8a295a6caac2950148bb160b457aff72437eadb SHA1: c5d75d04b01f681ead660ce8d8fe068ab887fba0 MD5sum: 8fbf7c362fd4091a78c50404eb694402 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.77.02.dfsg-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9277 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-pyo, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.77.02.dfsg-1~nd70+1_all.deb Size: 5822190 SHA256: c10fee5b4ff9f77c68eb2e39752c47a2e8f1ba8f4510fa31adf473ddb1ff1bdd SHA1: 2f2a47167fb4144c2f470d6a919f4031727386c1 MD5sum: 32542a621060f89c5b68058c8a1b2fec Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6, 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20130711.dfsg1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49324 Depends: neurodebian-popularity-contest Recommends: subversion Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20130711.dfsg1-1~nd70+1_all.deb Size: 19883476 SHA256: 773c1a81c83c83ae8b3480db148e953b9d440fde91f958016f8a62562443e345 SHA1: 48341fa911dcd0e4f33159875bbf186fe1972799 MD5sum: 9b10a8dacc9553f5a03a70b2e9b759d3 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: python-brian Source: brian Version: 1.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd70+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.4.1-1~nd70+1_all.deb Size: 549130 SHA256: 7c9586033503713d95ee640005799fd631ebc23b9857fa54739f713c13945ddc SHA1: 8df0debf188bacd59bf8e48470edc079ea401c5b MD5sum: 4d38a81ea37270a2ac871681b3c124b6 Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6798 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.4.1-1~nd70+1_all.deb Size: 2245550 SHA256: bbff81c2bf503166de3140452772e3684fcef15a172b12d230b1199a7333719d SHA1: 141fc07a2f6c5c65177d701a0612d7e1ba06f65d MD5sum: 99633747e0f9db0d78f345974120c115 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-cfflib Source: cfflib Version: 2.0.5-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 768 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-numpy, python-networkx (>= 1.4), python-nibabel (>= 1.1.0) Recommends: python-nose, python-sphinx, python-tables, python-h5py Provides: python2.6-cfflib, python2.7-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd70+1_all.deb Size: 217682 SHA256: 315d0c9976626dc452d7a4f03c9ff782c4caa12e182713db2c33d71233777b37 SHA1: 2f09d150c91742140a16fba4f03eceb4ad364e04 MD5sum: 34ba30e9fe7f1e59a608e67b241ca26c Description: Multi-modal connectome and metadata management and integration The Connectome File Format Library (cfflib) is a Python module for multi-modal neuroimaging connectome data and metadata management and integration. . It enables single subject and multi-subject data integration for a variety of modalities, such as networks, surfaces, volumes, fiber tracks, timeseries, scripts, arbitrary data objects such as homogeneous arrays or CSV/JSON files. It relies on existing Python modules and the standard library for basic data I/O, and adds a layer of metadata annotation as tags or with structured properties to individual data objects. Package: python-dicom Source: pydicom Version: 0.9.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1818 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.7-1~nd70+1_all.deb Size: 425116 SHA256: 94dbe117effdd1d6037241c583b97e0b8635d42e92b7b3a8d3bae5355de4b429 SHA1: 7d55bb2c43b2acc7c21f24465f769ffa6b26b7f9 MD5sum: f563e72d5cd375ab2a51a3e80ff81ba8 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2286 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.6.0-1~nd70+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy, python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.6.0-1~nd70+1_all.deb Size: 1588494 SHA256: 1f3b1b8a1d8994f60fdc5e0a281fafccef47836fae87a852d4ab2f1d1d9a0bc9 SHA1: 163f3a76886b730586c64bffc1e19d3e400252ff MD5sum: 7941b48d0f8642ecb924f0e1ee491de7 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.6, 2.7 Package: python-dipy-doc Source: dipy Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5068 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.6.0-1~nd70+1_all.deb Size: 3613464 SHA256: 56581cfaca7dc36cd0f681e04f3c495087f3f2cad28b550a4bf27e56bc93591b SHA1: 8ca22f8998d988c4b9bd996f89c4f3aae96f52c7 MD5sum: 9514848dcc0b6070ae06bcda092d46f9 Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-joblib Source: joblib Version: 0.7.0+git14-g3e5784c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 181 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.7.0+git14-g3e5784c-1~nd70+1_all.deb Size: 54644 SHA256: 84652f08973583119709bb2f4f6ced550bf7d96fe4eb779fb4c919ce92a1d3bb SHA1: 98daeb1d4fb28d8507f9beeb1941556f1dcc41fd MD5sum: d228449bbddb611947f182ddd05dd9c1 Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. Package: python-lazyarray Source: lazyarray Version: 0.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd70+1_all.deb Size: 7334 SHA256: 72dadd7fab4a8d37309793af8b50d73a7ea93f6c223509fe58ad502936fa852d SHA1: 3a45ca7b469e524691c3ed6ec708b24bd59391a8 MD5sum: 80d3117e7a8b1fa74d6551c6f2f306ed Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-mdp Source: mdp Version: 3.3+git6-g7bbd889-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1528 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git6-g7bbd889-1~nd70+1_all.deb Size: 484140 SHA256: 6928bc39f2c3b0888f5e6b236fc8cf8a677728cd26c6e7e1442cb3341a5f63dd SHA1: 900bed7f5e2e0f63a068ff302687b74ca12d92d8 MD5sum: ec7476a0bc0f232595e810a15fd1f239 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mpi4py-doc Source: mpi4py Version: 1.3+hg20120611-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 275 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3+hg20120611-2~nd70+1_all.deb Size: 76742 SHA256: a5f659c7aa363b8a241c7d2c35df15c95a321b2843614f0e503355a006582204 SHA1: 290de8ebc24c6a03b8152d6b6a9d00fe789ffe43 MD5sum: 231fb80c68d2061d1720fd4eb09ea42b Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd70+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa, python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd70+1_all.deb Size: 2204982 SHA256: d11d2301a31c5906b71d199f1d0c084f8b9cf9ac33bb537e24ab2b469b9099a4 SHA1: b362bf026b65424993dc7e63229b8670b55f487c MD5sum: e1bcf9e0206de77156760bbd52d0452f Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6, 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37572 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd70+1_all.deb Size: 8475162 SHA256: 650e2c780f78250bf58fada5c40a799f5b05cc59c640faac1f210075f4dc4102 SHA1: 01df95b2235666e3922f97ccfc582d42fa04e77d MD5sum: 6f013cc65b4edae93e4b62095cf568eb Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa2 Source: pymvpa2 Version: 2.2.0-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4242 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python-mvpa2-lib (>= 2.2.0-3~nd70+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, shogun-python-modular, liblapack-dev Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.6-mvpa2, python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.2.0-3~nd70+1_all.deb Size: 2400384 SHA256: b31ea29f8331d3e354bdbc07ac0be0e6357cf3795c9ffe49e3aadc2ea468459b SHA1: 04ae7e060abba18004cd410bdc3e17e4d96791a9 MD5sum: 8b71001392abfa89e757b361f73b517c Description: multivariate pattern analysis with Python v. 2 PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. . This is a package of PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.6, 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.2.0-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 17238 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.2.0-3~nd70+1_all.deb Size: 5158142 SHA256: 2b3333a25548f7c21dedfdf980f2ed7e7f25e7dc483076683cbedd8c229bd1bd SHA1: 111b43d7bbda7bc448007a739f40c60638b11d32 MD5sum: b4cad881befe599350b711300deb04f5 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-neo Source: neo Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2473 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.0-1~nd70+1_all.deb Size: 1434886 SHA256: e6de592c96e96aa4ff79ef6e59890927d706f5d5802025af707db3dd8dd0b4b9 SHA1: d8dde9436d375baf70a63e8d38000d36bd97cf99 MD5sum: 4bfa684acd1ba2929c3b13629728b26a Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-networkx Version: 1.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2672 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-numpy, python-scipy, python-pygraphviz | python-pydot, python-pkg-resources, python-matplotlib, python-yaml Homepage: http://networkx.lanl.gov/ Priority: optional Section: python Filename: pool/main/p/python-networkx/python-networkx_1.4-2~nd70+1_all.deb Size: 647240 SHA256: d330d947a368e24c1c211bb38680d39b541734610380b2eae4295581dc4cd792 SHA1: b2038a2f713e9b53f792369bacc2b37b26f406e1 MD5sum: 80ada5a82a23d92f2ce8d69d952d4f7f Description: tool to create, manipulate and study complex networks NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. Package: python-networkx-doc Source: python-networkx Version: 1.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15840 Depends: neurodebian-popularity-contest Homepage: http://networkx.lanl.gov/ Priority: optional Section: doc Filename: pool/main/p/python-networkx/python-networkx-doc_1.4-2~nd70+1_all.deb Size: 6234176 SHA256: 8a284c712351861f561505f6f7a85a6d6b86732f9020951066fca67be022c7a9 SHA1: d7da2a947abc8026e87191c4ff5893cdbd013adb MD5sum: d0470a135f7b7ae6fbb4252e2b688f86 Description: tool to create, manipulate and study complex networks - documentation NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. . This package contains documentation for NetworkX. Package: python-neurosynth Source: neurosynth Version: 0.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd70+1_all.deb Size: 32506 SHA256: e312e91ad5c3a552adf31946ac5d1c8b49511eacb9672d6d3e5fe95c67d2cf47 SHA1: 7c9148867c7d28312897d698b64bae996c8cd021 MD5sum: b003531676715edf0f1e1a459f3d6084 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 1.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4159 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Provides: python2.6-nibabel, python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.3.0-1~nd70+1_all.deb Size: 1826560 SHA256: d20bf8c6f53a1db782a253210648f0087c4400b973796d8320f686e390598eea SHA1: d59caeb156195f2e1402f01c2056b9c5d332b583 MD5sum: 980b87e70877228791009f66cf1d8a84 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Python-Version: 2.6, 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2437 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.3.0-1~nd70+1_all.deb Size: 445200 SHA256: 05188099e45d95bae43f2ee2252dd56fcf9c846225a3a2b28d5c10a2d0373731 SHA1: 7b3495292d877b7d4521313272d64d57fedfaa0d MD5sum: 3adb28b0629dbb6cee11a5ad524ff0db Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nipy Source: nipy Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2865 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.2), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0-1~nd70+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.6-nipy, python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0-1~nd70+1_all.deb Size: 785872 SHA256: b7ba5bef343a9b430063fd264d9613761d69bbbfad13d9a02167233650dad402 SHA1: c9f9908cc8e0a37704729d010557e12b4ddc8a9e MD5sum: de2ccfc8e8f4857a80a4697c608ad077 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.6, 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10302 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.3.0-1~nd70+1_all.deb Size: 3930146 SHA256: df91eb4cd8ff5cc29a288368742ff18cb2f06e73dff892aacf875a0f13716b2e SHA1: 03e2b3aac192438444be9679ddf4410e92ec3ca3 MD5sum: 8bd743d2c3a7ebdb6d54098595b1f86b Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipype Source: nipype Version: 0.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2657 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.6-nipype, python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.8-1~nd70+1_all.deb Size: 591974 SHA256: 55589d21fb0122bc08e2e5a218f57f76372a878e7759093670dde664e33cbc48 SHA1: 5ed73db079f62e183d3dc44a020c17cf7f01b6e1 MD5sum: e81e230a669db4a93139c5cff3d888c3 Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14913 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.8-1~nd70+1_all.deb Size: 7058474 SHA256: b11f57c56adc40dfb58c517fecce3f39eec51e0ee36509676339603e26c9870a SHA1: 104058e90d4484d02f3b7a88ba15ba3fa3aa6226 MD5sum: 0e13f245c90c6d32870b4919e63afd08 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9294 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.4-2~nd70+1_all.deb Size: 3908944 SHA256: a63f5d2e326aaae30fa5fdb454b60a318a50a40393fba73dc363181011ed1659 SHA1: 1761b8e7c0fff9bb0335cfc98d091880bd84c2aa MD5sum: 897e0f8e7899efc50bd2a47eddb1d9f6 Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6833 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.4-2~nd70+1_all.deb Size: 5336052 SHA256: 7bba66ea7db9fb89cc4db044f9de52047d2678c203ecce6fea099fd14c4eca9d SHA1: a8fef6de47b4d9b28da4e48b28c4404cd4e794a5 MD5sum: cf70230e3055f11c4b85a0a3bf429ba8 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-numpydoc Source: numpydoc Version: 0.4-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 123 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-sphinx (>= 1.0.1) Suggests: python-matplotlib Homepage: https://github.com/numpy/numpy/tree/master/doc/sphinxext Priority: optional Section: python Filename: pool/main/n/numpydoc/python-numpydoc_0.4-1~nd70+1_all.deb Size: 30716 SHA256: 8648d709597fb78a38f6841b93b85eaae140766681e339038a65d36b540fb613 SHA1: f768d4b4ce1d0584845f1f240f4a0209485645d7 MD5sum: 7b6112f1cb570e854c22fc8c8ddba749 Description: Sphinx extension to support docstrings in Numpy format This package defines several extensions for the Sphinx documentation system, shipped in the numpydoc Python package. In particular, these provide support for the Numpy docstring format in Sphinx. Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt, python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.38+svn1589-1~nd70+1_all.deb Size: 245060 SHA256: 19a135e4be8de62b737ca038370ef26c98892482f2291ec50c700b1ca2a5c996 SHA1: 847bd52591836b097723a48e910c63f5abb60272 MD5sum: f4ba9ac3e1c8940039fdb02678385adb Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6, 2.7 Package: python-openpyxl Source: openpyxl Version: 1.6.1+hg2-g4bff8e3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 291 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.6.1+hg2-g4bff8e3-1~nd70+1_all.deb Size: 62034 SHA256: 3b90e8687f43ebfb82da0f9ffb48cd7bfc65547ce6231ee45b3d47722bc0d2bb SHA1: ff37abb51ce94f774f881cdacdcbcdcf6cbb3770 MD5sum: 6834ae5c32934ac734a40b4e159f0b39 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pandas Source: pandas Version: 0.11.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4672 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.11.0-1~nd70+1) Recommends: python-scipy, python-matplotlib, python-tables, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt Suggests: python-pandas-doc Provides: python2.6-pandas, python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.11.0-1~nd70+1_all.deb Size: 953532 SHA256: 875d4ba450228f12fd4f38568e8af8689db759172b29ae422d51db0a8b811f00 SHA1: c1dc92f2cee19512e6eccf7bd6c7e1a94aa1cc32 MD5sum: 13524877338d930afb87d8622d99162b Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 2 version. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd70+1_all.deb Size: 34272 SHA256: 076297344fdb2aad569d128266cbb592689458ac0e2ec4d78a5e8ca14bf8d5b7 SHA1: 910e6bf6e2bb4575f1e378cb1af24d0f91b2bd44 MD5sum: ed9536ef265e9d7e3cd7356d561e2f60 Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.6, 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 884 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1~nd70+1_all.deb Size: 107932 SHA256: 9e2808d481734f4f0937fb9a468d30716a8eb811518d684abf9844ee21ee8a4a SHA1: d4ab6e31eadff85c7e7f8b7220cb97c7d66d303d MD5sum: 33924f1ceaba1a3a3ba22f172ff8a0d1 Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.6, 2.7 Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy, python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd70+1_all.deb Size: 21330 SHA256: af5c1ea7542c31abb491d792b1bfaef5d5a74aef7402c4659297bec687394d72 SHA1: d0b06b12f69cf46fc8a2db6c3ec5cdc548da2fe0 MD5sum: fbbf7aeb5538f3b546599d3eb9e9a81b Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.6, 2.7 Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-1~nd70+1_all.deb Size: 818162 SHA256: 90aed61f6075ac5506e61e7074563588c074fbb94b6382d8f6b41149be57af1f SHA1: da93452615d933402b648f06b9e42ed025629b12 MD5sum: 72ca3455e663ab8ac1596c6cc5670169 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pymc-doc Source: pymc Version: 2.2+ds-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1840 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.2+ds-1~nd70+1_all.deb Size: 903858 SHA256: e58138742a6d440f1e36740ba231cfafd2740becf4917a1fc3554258e8a243ac SHA1: 32d42e11b09c7b7959422447a866a01cc90f2610 MD5sum: 0d85f78c49384678bdad75cbfa1d44ea Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pynn Source: pynn Version: 0.7.5-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd70+1_all.deb Size: 192128 SHA256: 3ed89b456870d6b6530e6662b034a3906298a8b612109135b96518fc3837c8bc SHA1: fa36b5bb19a5cf7b87a4fe9d12d43fccd90b1844 MD5sum: fc397ee0c6e5376bda371cc680f0c56a Description: simulator-independent specification of neuronal network models PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-pyxid Source: pyxid Version: 1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Homepage: https://github.com/cedrus-opensource/pyxid Priority: optional Section: python Filename: pool/main/p/pyxid/python-pyxid_1.0-1~nd+1_all.deb Size: 11020 SHA256: 1031c0d69dd73cb38f3e0b826193211706a94bfd04da4287288418b257e54249 SHA1: 0f0d0524354e5d07eb89efcb11779d9acd9d57e2 MD5sum: 1f2a9bc07952b1f5c6b65fc5c092f75c Description: interface for Cedrus XID and StimTracker devices pyxid is a Python library for interfacing with Cedrus XID (eXperiment Interface Device) and StimTracker devices. XID devices are used in software such as SuperLab, Presentation, and ePrime for receiving input as part of stimulus/response testing experiments. . pyxid handles all of the low level device handling for XID devices in Python projects. Package: python-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1722 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd70+1_all.deb Size: 376574 SHA256: f3143d606791308341d10dd7752b4f8a89d4d962ddc1bfdfb43324c11b19e0fb SHA1: b35f0b369867653fb22853d37c7b2e56825267ae MD5sum: c172162c217fd132f93dfebf701445c5 Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-quantities Version: 0.10.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd70+1_all.deb Size: 62650 SHA256: 7105f0be0bad6a6896943c81ffc4f7ebd4e7ce36829bf3747f8fbb603246e059 SHA1: c36035905534efefa681ab02a9b30a297c46c3fc MD5sum: 370baf01ebbe89b0e73e46b3b3dee9e2 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported. Package: python-scikits-learn Source: scikit-learn Version: 0.13.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.13.1-1~nd70+1_all.deb Size: 28604 SHA256: f2d7aad5116e28c4442d38113f34e07c0eaff624bf8969d5525d1db7f1ad24ce SHA1: 0195b8fddb4deff62e8b709ed5aee0f738425628 MD5sum: be2931caa8f9c374c350632d2be6a84d Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.6-simplegeneric, python2.7-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd70+1_all.deb Size: 9810 SHA256: c0bf53d256b2a9520f7c40efd3af9d01c92802949256bdc3ddcbe6f8c809ba45 SHA1: b9a5abab569c8269207372b91c7e89a7230efc84 MD5sum: 46e1c70528d4fd5c5636ec720f54787f Description: Simple generic functions for Python The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library. Package: python-skimage Source: skimage Version: 0.8.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4550 Depends: neurodebian-popularity-contest, python (>= 2.6), python-numpy, python-support (>= 0.90.0), python2.6, python-scipy (>= 0.10), python-skimage-lib (>= 0.8.2-1~nd70+1), libfreeimage3 Recommends: python-nose, python-matplotlib (>= 1.0), python-imaging, python-qt4 Suggests: python-skimage-doc, python-opencv Provides: python2.6-skimage, python2.7-skimage Homepage: http://scikits-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.8.2-1~nd70+1_all.deb Size: 3236984 SHA256: 4cc4e72a077f813caa0f133b07fcbf853e70be393ecd116c3b3eb2afe80ee388 SHA1: d8b3de267988fdd0cd941950805fed0f9b621ed6 MD5sum: 58c15b49a237e4bf5fbb8840c97cfa86 Description: Python modules for image processing scikits-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. Package: python-skimage-doc Source: skimage Version: 0.8.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14193 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-skimage Homepage: http://scikits-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.8.2-1~nd70+1_all.deb Size: 11823562 SHA256: 741c4e348522a251cacc1904c8b2b39eb40a94247654f3d6621245a8e3a31577 SHA1: 88c1efd39024f137d2641babf0bba0c79419fa4f MD5sum: 442a5fc7b4e0138a641dd1fa9c2be83a Description: Documentation and examples for scikits-image This package contains documentation and example scripts for python-skimage. Package: python-sklearn Source: scikit-learn Version: 0.13.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3050 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.13.1-1~nd70+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.6-sklearn, python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.13.1-1~nd70+1_all.deb Size: 1012766 SHA256: 80592b86ea6603b56a2ec8ec3837d6a2e9cfce132122f3b136ca43d33227daf8 SHA1: ac95a5b436322f8ba22bc40b4ef3eedc1b69cd57 MD5sum: 1e4414d8292d699d29816e6573840eec Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Package: python-sklearn-doc Source: scikit-learn Version: 0.13.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42486 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.13.1-1~nd70+1_all.deb Size: 31108638 SHA256: a2231b3f073fa65b06ba4257d0f16fc450d6db3dcaed93b16d1231565dafa869 SHA1: 92162ad8ee6381a95fa39503bde71bf460c1130c MD5sum: 556d6137909cdb2eb772a0bee0568473 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd70+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~nd70+1_all.deb Size: 1260232 SHA256: 648244da9a934daaee709edb7cd2d109551e93e215ebd43730a5a0bff017a035 SHA1: a878bb9a26d7085fd2ec3e02fa606ae3a44a9528 MD5sum: 9be86574fc484fd49d5be81bd6deba03 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-spykeutils Source: spykeutils Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1976 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.3.0-1~nd70+1_all.deb Size: 391988 SHA256: 750f5cb45437a4dec6e071d5d6d7f6bfa728017cada86c38e2efbc0f52f57e57 SHA1: 78a91a21f39773f9d2272c2258462294344ee313 MD5sum: 1dbd194db0abb930e8f8ab8327390da3 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.4.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12433 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-statsmodels-lib (>= 0.4.2-1~nd70+1) Recommends: python-pandas, python-matplotlib, python-nose, python-joblib Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.6-statsmodels, python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.4.2-1~nd70+1_all.deb Size: 3104854 SHA256: 7a1d2cc98556f87d43ece0c055bdd3faea477245d337417bce401247cbb65845 SHA1: 96a207a7bc359d5d3a17cd6623ad3ef1d4be494b MD5sum: 8f67e249f43934e8bb54e5a87e67fa0d Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.4.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23638 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.4.2-1~nd70+1_all.deb Size: 7340256 SHA256: 0d91a4378206f49a2a1b171c51a7c8dc87fdaf6f97696c54c1007eb4b2b2401d SHA1: 73b793db2401f3e767911f5ba747ac00d83ab4d6 MD5sum: 63f2796b4e2b081f3c15f0c2168d5c57 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-surfer Source: pysurfer Version: 0.3+git15-gae6cbb1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 95 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.3+git15-gae6cbb1-1~nd70+1_all.deb Size: 28900 SHA256: 4df80f80d2fed01ef90c5a916faa87e6b6a6a8b5a3c2e659f25c1ea01ced3924 SHA1: 835637f3ec260432045cda17ac0eec30b2cc0cca MD5sum: 1b138ca525cf57a2410e515ea217bbe7 Description: visualize Freesurfer's data in Python This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. . PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets. Python-Version: 2.6, 2.7 Package: python-tz Version: 2012c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 138 Depends: neurodebian-popularity-contest, tzdata, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd70+1_all.deb Size: 39000 SHA256: 4d99b0c0de79ceca4b307484afb320bed4f244d51252ae87a29f931d16f93959 SHA1: 67aa4d3871f125fa3f04b2f0fddee56d9bcdb8db MD5sum: 7766a106c9f3ea0f29222f96da871952 Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Package: python3-pandas Source: pandas Version: 0.11.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4567 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.11.0-1~nd70+1) Recommends: python3-scipy, python3-matplotlib, python3-tables Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.11.0-1~nd70+1_all.deb Size: 943376 SHA256: 7006b789ac00a1669be17215ebb9d6704d7b6619a07a42f717c68fba52a01ca9 SHA1: 1f00f7b4559a736f23b43416d1e3bc1f1d520a24 MD5sum: 12db8679c15e051a34b3bf477fad0070 Description: data structures for "relational" or "labeled" data - Python 3 pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure . This package contains the Python 3 version. Package: python3-tz Source: python-tz Version: 2012c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Depends: neurodebian-popularity-contest, tzdata, python3 (>= 3.2.3-3~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd70+1_all.deb Size: 31954 SHA256: 3e97caf66172c67dea29b32d60a6a976e032f2e3cb18dfea5ec7bb0c1a7618af SHA1: 4c06117f76e0b1ad499102b3844bd8cf2357cb7a MD5sum: 464ec516d7b9cbcf1f82127ecd56ebb7 Description: Python3 version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). . This package contains the Python 3 version of the library. Package: shogun-doc-cn Source: shogun Version: 1.1.0-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1545 Depends: neurodebian-popularity-contest Recommends: shogun-python-modular, libshogun-dev Homepage: http://www.shogun-toolbox.org Priority: optional Section: doc Filename: pool/main/s/shogun/shogun-doc-cn_1.1.0-6~nd70+1_all.deb Size: 556068 SHA256: f8376758069c8e22fedb758202fea6063d95aa3aa4400f084c4f8e10b9118796 SHA1: 3f5b5ae50cc2dcf41c120bb995369dcda3e5cddd MD5sum: 44dcec822faa27167037f325ff2be792 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Chinese user and developer documentation. Package: shogun-doc-en Source: shogun Version: 1.1.0-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 85407 Depends: neurodebian-popularity-contest Recommends: shogun-python-modular, libshogun-dev Conflicts: shogun-doc Replaces: shogun-doc Homepage: http://www.shogun-toolbox.org Priority: optional Section: doc Filename: pool/main/s/shogun/shogun-doc-en_1.1.0-6~nd70+1_all.deb Size: 17119184 SHA256: 3f07ea2441ab9f83d787f60ddb9cd08f4fc9394f062ac584ffe7e2a14e9b437f SHA1: d0333cc59cb4433eefd2ba5123fe7384b6430041 MD5sum: 4462916c2cb8bd9f994d83f46f465022 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the English user and developer documentation. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18626 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd70+1_all.deb Size: 10751106 SHA256: 4b0892096fb3e6c5ba1254a3c3a218a92ae151e1a37fb8fc29dadbac8b624a6d SHA1: 0397da1f5bbd5171f4ef11c705679bd2a2915530 MD5sum: 283cc17b8f9c34af894c68533fe70a57 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73046 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd70+1_all.deb Size: 52177460 SHA256: 51fc6055c99b93fcf82446d3357a9b8143dee566714de2921103a58a61eef981 SHA1: 11a2d79617c8c0883acdfc4e3689baf240bcdb79 MD5sum: e3fb3e6df0f60a562696f6ad2a91b292 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9273 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd70+1_all.deb Size: 8991102 SHA256: e203c8227771f56005d1e04f7fbec1a7bfc58c5ba9dde1da5aa8bc32f434f9c2 SHA1: a984401fdd20fa64f68b76ec1fc06d73e6ed6b4c MD5sum: 3c6e980cbe8ec3bc7f268fcb98d177bf Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: spykeviewer Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 932 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.3.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.3.0-1~nd70+1_all.deb Size: 490510 SHA256: 8bef972c5f4b13915bd7731448187b86e92496c2659b9d317c4667902df86a68 SHA1: 8e73ca6c1a40ad0a35ade595bc9e9acb70c45f55 MD5sum: 46a63ea77f88b2fb12c0e5f67cf8c186 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd70+1_all.deb Size: 28600 SHA256: d06a1ee5b6de6404f66db07820f084ca9699bfcef21015bb34c9cd64e1900e74 SHA1: 6515b207f33e7ef2ea59d0db40bb2b35d39355b8 MD5sum: 365f3a53daff4820e153393bb90a269c Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.6, 2.7 Package: svgtune Version: 0.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-1~nd70+1_all.deb Size: 6828 SHA256: 664347bc9decb736aec4f14819a9eef0c8afedf8aae82d45087ff30facae72af SHA1: c2ca191c7b3cd09c05d737e60ed14c298dd3190e MD5sum: ac63ca302b7db2272aced98a86d44a08 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: testkraut Version: 0.0.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd70+1_all.deb Size: 100034 SHA256: 569f799af355429d7939adc34742caadb6f3eb108bb1a32b35cc5cabdb8336ca SHA1: e4a40dab2d773f92b8a810ba078d96d218775dcb MD5sum: 1a32c11b522abfa6f8b658c890f2cbe4 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.6, 2.7 Package: ubuntu-keyring Version: 2010.+09.30~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd70+1_all.deb Size: 11794 SHA256: c326d77f59c53ce386ed48a4f622087920af9c2d0a9b826e734680500b0cd3a0 SHA1: a46c68a0539f105919576423f0daeb6709e6a10a MD5sum: 8bed9b239d848186981a2e04eec03bb1 Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that. Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.2-1~nd70+1_all.deb Size: 50202264 SHA256: 3eb654b5c4218ac049bc44a57266938153990ee8de27f301a7e9187473a44461 SHA1: 001ffa867833ab2bf4e936dba0b6df559c527556 MD5sum: 58d474d55780043657b559bcbb641d7a Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit. Package: vtk-doc Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 342007 Depends: neurodebian-popularity-contest, doc-base Suggests: libvtk5-dev, vtk-examples, vtkdata Homepage: http://www.vtk.org/ Priority: optional Section: doc Filename: pool/main/v/vtk/vtk-doc_5.8.0-7+b0~nd70+1_all.deb Size: 66709864 SHA256: 1a71117b4f7574428e9da98482fb0c2cb41581e0ca6d2e931ea639a5da51263c SHA1: 74a40de489c5a44161b4aa468547d356da0bf911 MD5sum: 4c9a59935cca888f4c608d56f7eb3213 Description: VTK class reference documentation The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains exhaustive HTML documentation for the all the documented VTK C++ classes. The documentation was generated using doxygen and some excellent perl scripts from Sebastien Barre et. al. Please read the README.docs in /usr/share/doc/vtk-doc/ for details. The documentation is available under /usr/share/doc/vtk/html. Package: vtk-examples Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2521 Depends: neurodebian-popularity-contest Suggests: libvtk5-dev, tcl-vtk, python-vtk, vtk-doc, python, tclsh, libqt4-dev Homepage: http://www.vtk.org/ Priority: optional Section: graphics Filename: pool/main/v/vtk/vtk-examples_5.8.0-7+b0~nd70+1_all.deb Size: 578898 SHA256: d070189a36ffd5bed00de02b3c794d0fa8f8bb2765fbc36f0f99c1634cda5ac7 SHA1: 132096d02c71c2f969d9baff0842e4afcdbb501c MD5sum: c018d4c1cace1b218dec239a1cf5e39e Description: C++, Tcl and Python example programs/scripts for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains examples from the VTK source. To compile the C++ examples you will need to install the vtk-dev package as well. Some of them require the libqt4-dev package. . The Python and Tcl examples can be run with the corresponding packages (python-vtk, tcl-vtk).