Package: bats Version: 0.4.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest Homepage: https://github.com/sstephenson/bats Priority: optional Section: shells Filename: pool/main/b/bats/bats_0.4.0-1~nd70+1_all.deb Size: 15366 SHA256: 2a32b894f1a426e1effcecdac7afb898992c41a99a32c5b7885626f12b349d10 SHA1: 7696e4469ab1f974dccfe7c782384965e2e80d2c MD5sum: ed9c98c88ecce9dac667d78b77559f25 Description: bash automated testing system Bats is a TAP-compliant testing framework for Bash. It provides a simple way to verify that the UNIX programs you write behave as expected. Bats is most useful when testing software written in Bash, but you can use it to test any UNIX program. Package: btrbk Version: 0.22.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 242 Depends: neurodebian-popularity-contest, perl, libdate-calc-perl, btrfs-tools (>= 3.14) Suggests: openssh-client Homepage: http://www.digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.22.2-1~nd70+1_all.deb Size: 60306 SHA256: d617bb29bf4ca6ca11138d96dfbc6c7576af9153cc3e64e6628a26833772a643 SHA1: 497565b5b4cf7e95153775e8dd1b480a982413d0 MD5sum: 824e9955c1677e0d12d8e43ac7bbf5e4 Description: backup tool for btrfs volumes Backup tool for btrfs volumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations at once, with ssh and configurable retention support (daily/weekly/monthly). Package: condor Version: 8.2.8~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 45 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.2.8~dfsg.1-1~nd70+1_all.deb Size: 15550 SHA256: 12d06578acad6d01e11069e48673d52508364cb34307ca0f4d22ab3fccd1157b SHA1: 9252c271dc8926cd0cc156404e9dc7950ec8281a MD5sum: ad42dc22107d1c9a69b56010cdd3f51b Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dbg Source: condor Version: 8.2.8~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 45 Depends: neurodebian-popularity-contest, htcondor-dbg Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dbg_8.2.8~dfsg.1-1~nd70+1_all.deb Size: 15556 SHA256: c9362d9efb09d48d73a9bc70b0a39405f1beeab3b0d42c61334070d2fbdf87ec SHA1: 76844cfbaeb7b7a68cbae859947941c33d54b795 MD5sum: 4e5465c1c1fbce12a1175a90658b46d0 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dev Source: condor Version: 8.2.8~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 45 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.2.8~dfsg.1-1~nd70+1_all.deb Size: 15556 SHA256: db3da1c898398ad23445973ef3b282883c4e86f5d00156ba4d253f36f7e4400e SHA1: 98e18a39ab27d0a71d980a7dd65e507c7720701b MD5sum: 43e84f451090a3f7886afea1037468a6 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-doc Source: condor Version: 8.2.8~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 45 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.2.8~dfsg.1-1~nd70+1_all.deb Size: 15558 SHA256: e592eddc75427d5e50de65108d9266f99b7bd9801f70001ed692b8be12a52fb5 SHA1: 06a614cc1b42c1a2c232ba9025715600895fdb6f MD5sum: bcccbc1bdfeb3d84307eeae36e54aede Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. 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: dh-python Version: 1.20131021-1~bpo70+1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 227 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~) Priority: optional Section: python Filename: pool/main/d/dh-python/dh-python_1.20131021-1~bpo70+1~nd70+1_all.deb Size: 63466 SHA256: 877c1a30fbb0c52443e860255912ffcfb9ae9a31da796b6309fe6b1f08e15cb1 SHA1: 28786b4c55b41be8544285f6f0c7f8e8c265d89c MD5sum: 5bb11f7e1e42aac34e67c025f353039f Description: Debian helper tools for packaging Python libraries and applications This package contains: * pybuild - invokes various build systems for requested Python versions in order to build modules and extensions * dh_python2 - calculates Python 2.X dependencies for Debian packages, adds maintainer scripts to byte compile files, etc. * dh_python3 - calculates Python 3.X dependencies for Debian packages, adds maintainer scripts to byte compile files, etc. * dh_pypy - calculates PyPy dependencies for Debian packages, adds maintainer scripts to byte compile files, etc. Package: dh-systemd Source: init-system-helpers Version: 1.18~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd70+1_all.deb Size: 14630 SHA256: bedf7f47ba2292bae5f532bcb9125cab3e1a3e4ac1d1e4bdeeb4ebfe10efc1c3 SHA1: 1b9cc0b9731616b968aa0ab95b59fa96f3ff9d13 MD5sum: 8be99fbb0699e021da9a760777f36c2f Description: debhelper add-on to handle systemd unit files dh-systemd provides a debhelper sequence addon named 'systemd' and the dh_systemd_enable/dh_systemd_start commands. . The dh_systemd_enable command adds the appropriate code to the postinst, prerm and postrm maint scripts to properly enable/disable systemd service files. The dh_systemd_start command deals with start/stop/restart on upgrades for systemd-only service files. 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: 0.9.4-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1432 Depends: neurodebian-popularity-contest, python (>= 2.6.6-3), python (<< 2.8), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: mailx, system-log-daemon, monit, python-systemd Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.9.4-1~nd70+1_all.deb Size: 337596 SHA256: 5d8772219960d1ee356cdf6032915ca2e6604ef0bcd6eb489739e10e2f7c6520 SHA1: 4777d652e9f75934bcb277443c1f4d2e6a751ab6 MD5sum: 9eb67c0e116e6f73a2972426792c2459 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. Following recommends are listed: . - iptables -- default installation uses iptables for banning. You most probably need it - whois -- used by a number of *mail-whois* actions to send notification emails with whois information about attacker hosts. Unless you will use those you don't need whois - python3-pyinotify -- unless you monitor services logs via systemd, you need pyinotify for efficient monitoring for log files changes Package: fis-gtm Version: 6.0-003-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, fis-gtm-6.0-003 Provides: mumps Homepage: http://sourceforge.net/projects/fis-gtm Priority: optional Section: database Filename: pool/main/f/fis-gtm/fis-gtm_6.0-003-2~nd70+1_all.deb Size: 15108 SHA256: 8a48d53f74d1f1705844ba5ddad14a6f75f3588c56c92e5de6cd0dbda3952598 SHA1: 615f1962b57b77ab49d8ab6702fc989cb40b409a MD5sum: c3fb09773b5eb09dd6cadbbdc024e5b8 Description: metapackage for the latest version of FIS-GT.M database GT.M is a database engine with scalability proven in large real-time transaction processing systems that have thousands of concurrent users, individual database file sizes to the Terabyte range (with virtually unlimited aggregate database sizes). Yet the light footprint of GT.M allows it to also scale down for use in small applications and software appliances (virtual machines). . The GT.M data model is hierarchical associative memory (i.e., multi-dimensional array) that imposes no restrictions on the data types of the indexes or content - the application logic can impose any schema, dictionary or data organization suited to its problem domain. (Database engines that do not impose schemas, but which allow layered application software to impose and use whatever schema that is appropriate to the application are popularly referred to as "document oriented", "schemaless" or "schema-free" databases.) . GT.M's compiler for the standard M (also known as MUMPS) scripting language implements full support for ACID (Atomic, Consistent, Isolated, Durable) transactions, using optimistic concurrency control and software transactional memory (STM) that resolves the common mismatch between databases and programming languages. Its unique ability to create and deploy logical multi-site configurations of applications provides unrivaled continuity of business in the face of not just unplanned events, but also planned events, including planned events that include changes to application logic and schema. . This metapackage always depends from the default fis-gtm version. Package: freeipmi Version: 1.4.9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd70+1), freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.4.9-1~nd70+1_all.deb Size: 1122 SHA256: 760ec4be7b50fdbf5310c97bbfd29986f7d0c6f5ecb1781f12e18ec461d54831 SHA1: 1b87081846b6d5d371cb1774c14c8f37d6d9c90e MD5sum: 5880c9684719ff72e16a96b133e4f7cb 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 metapackage depends on all separate modules of freeipmi. Package: freeipmi-common Source: freeipmi Version: 1.4.9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 451 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_1.4.9-1~nd70+1_all.deb Size: 346136 SHA256: 7c83c6c36fb5ee831c625242ee5afc0e4362d1623307815558e8b708fa22c122 SHA1: d756a6943a9b6d91838075927fbe2b70b0431261 MD5sum: c8737ba0a120c1f3d45839b573327f2c 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: fsl-melview Source: melview Version: 1.0.1+git9-ge661e05~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 146 Depends: neurodebian-popularity-contest, python-scipy, python-matplotlib, python (>= 2.6.6-3), python-nibabel, python-numpy, python-pkg-resources, python (<< 2.8), python-pyface, python-traits, python-traitsui, python-enthoughtbase Suggests: fsl-core Homepage: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Melview Priority: optional Section: science Filename: pool/main/m/melview/fsl-melview_1.0.1+git9-ge661e05~dfsg.1-1~nd70+1_all.deb Size: 15820 SHA256: c9edf062db46754e401360cc2108bd3bbb0f27d1d1af4dce0e0ce5ceb46563ce SHA1: 90f2182a486de64f8faa89c2b352383b71d81922 MD5sum: 3b5533d566430cb10f0c189371ec87b6 Description: viewer for the output of FSL's MELODIC This viewer can be used to facilitate manual inspection and classification of ICA components computed by MELODIC. As such, it is suited to generate hand-curated labels for FSL's ICA-based denoising tool FIX. Python-Version: 2.6, 2.7 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: 1.1.5-1~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_1.1.5-1~nd70+1_all.deb Size: 16578 SHA256: 67088e7164c36ef9252ad4cbfe6c844c8d743d262951ff4e4c326ec757aa78b5 SHA1: f448553684f801378d706b67eb873bfb7579ac15 MD5sum: 2a15157f72f9ee1d82110c7c1375a1f4 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 Source: guacamole-client Version: 0.8.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 475 Depends: neurodebian-popularity-contest, guacd Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole_0.8.3-1~nd70+1_all.deb Size: 428518 SHA256: 0d8d7e00f9bea518612ae40596246823279ff8d7c1126ec10beef8848c00a385 SHA1: bdd2e798af468ea8cbf87d32a8b113c7a0b72c57 MD5sum: 153ddcf74c34d7c6195d669dc5992716 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-client Version: 0.8.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole-tomcat_0.8.3-1~nd70+1_all.deb Size: 6944 SHA256: a0f7f85710d74708386d4be22e68c0ceeafe410755930f075c49648962c5684a SHA1: 663164e7be20b679af98ec91fb8ebe16f5b8917b MD5sum: 915d0c1d4bb6540aab4ec1066e39e486 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: heudiconv Version: 0.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 79 Depends: neurodebian-popularity-contest, python, python-dcmstack, python-dicom, python-nibabel, python-numpy, python-nipype Recommends: mricron Homepage: https://github.com/nipy/heudiconv Priority: optional Section: science Filename: pool/main/h/heudiconv/heudiconv_0.1-1~nd70+1_all.deb Size: 10874 SHA256: a0bc382c1c1ca41edef3b954c91cee8530b851feeed9addaac5f4d174edaf81b SHA1: 7b8845aa5cfc5531c9a6b0fe1b1a5996b77e7310 MD5sum: d2474cc6c51da55868e16181d36fb33a Description: DICOM converter with support for structure heuristics This is a flexible dicom converter for organizing brain imaging data into structured directory layouts. It allows for flexible directory layouts and naming schemes through customizable heuristics implementations. It only converts the necessary dicoms, not everything in a directory. It tracks the provenance of the conversion from dicom to nifti in w3c prov format. Package: htcondor-doc Source: condor Version: 8.2.8~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5700 Depends: neurodebian-popularity-contest Breaks: condor-doc (<< 8.0.5~) Replaces: condor-doc (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: doc Filename: pool/main/c/condor/htcondor-doc_8.2.8~dfsg.1-1~nd70+1_all.deb Size: 1397790 SHA256: 8f03b5a5861b1b4c6d79f2e0b42f257fb8f2f5637234f9b637f049c08a7780c3 SHA1: a6391c6757594f6350f46e6ed5850e7632a3acba MD5sum: 52b0398f1dcc383871e5329586ef5688 Description: distributed workload management system - documentation Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor 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, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package provides HTCondor's documentation in HTML and PDF format, as well as configuration and other examples. Package: impressive Version: 0.11.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 470 Depends: neurodebian-popularity-contest, python, python-pygame, python-pil | python-imaging, poppler-utils | mupdf-tools | xpdf-utils (>= 3.02-2) Recommends: mplayer, pdftk, perl, xdg-utils 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_0.11.1-1~nd70+1_all.deb Size: 191406 SHA256: 3af6beef9ba3350e3f723b74247dff56b10879e099536a2597f9e6c214cabe43 SHA1: 974748224418a4c8aaf81414b8f7ae93f0af691b MD5sum: 28639d1eb13fe7b535e85490dc11c30e 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 * Support of movies presentation * Active hyperlinks within PDFs 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: init-system-helpers Version: 1.18~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, perl Breaks: systemd (<< 44-12) Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/init-system-helpers_1.18~nd70+1_all.deb Size: 14306 SHA256: a70fc6126cb55857e2dbcb3b3068a3003fab56f54640fe85140a477943bdbf17 SHA1: b9aaea635981bc1d0d3d14fc1bc2c0911024e369 MD5sum: 5c7feaf7e6d24d453d49aa549a530d40 Description: helper tools for all init systems This package contains helper tools that are necessary for switching between the various init systems that Debian contains (e.g. sysvinit, upstart, systemd). An example is deb-systemd-helper, a script that enables systemd unit files without depending on a running systemd. . While this package is maintained by pkg-systemd-maintainers, it is NOT specific to systemd at all. Maintainers of other init systems are welcome to include their helpers in this package. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.5.0-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2672 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_4.5.0-3~nd70+1_all.deb Size: 2405102 SHA256: 3ff83e44b9415f06d817801b64e38e19080a9c56025ddebe8dd2401c2f9952f1 SHA1: 8da3d0e8d88b240932b9f08138e5aafd068a08d6 MD5sum: 634b74017a90396baf729df0e0bdc393 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: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12043 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: ipython1x-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/ipython1x/ipython1x_1.1.0+git7-gf5891e9-1~nd70+1_all.deb Size: 4536746 SHA256: 852baf9b42738d3cb3610260a2172fb95197f347f5403d68470980ac1d58c022 SHA1: 0d89510fc1dedaa618407fa67d579ed6fa16bec9 MD5sum: 50bda66cac5a0b55216797762e2de43a 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 IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10388 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.1.0+git7-gf5891e9-1~nd70+1_all.deb Size: 4186970 SHA256: ffbd105da9db893459abf3d319ff7ebd58f492a3fad45738335c90488f65b3ca SHA1: fb31071ec088898eaac6fcc6e1c3e56f71236eca MD5sum: 9792dc5f34ffdb37466d60ac1bfe508e 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 IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.1.0+git7-gf5891e9-1~nd70+1_all.deb Size: 910 SHA256: a2dc21ce94ea07009c263c637733d436d784f065f23cb86f9ca1a9a478104346 SHA1: 3fa9be414b9acd8cee68e5a773ca27f109bbf048 MD5sum: c62ead473d9515cdf7314225fdbc1303 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x 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: ipython1x-parallel Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.1.0+git7-gf5891e9-1~nd70+1_all.deb Size: 836 SHA256: 6509f33db3634701939c662a8a3d66ff7695136545d7ae9555993e28542e3e8c SHA1: c85c4b2438715328245e44b2ad087cfb584c9159 MD5sum: 8a5cc2d4d0c568873da2f65fe69b4087 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.1.0+git7-gf5891e9-1~nd70+1_all.deb Size: 922 SHA256: 35eaab30109f0f388a6911664a74f73bdac66419cec5f0193274eef59eef7308 SHA1: 1a5f95fdc76899d101c6f67ec8d0a4475e05f281 MD5sum: 6f5fcf44c21bd72bfa12f0dcb8d07f85 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x 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: libdouble-conversion-dbg Source: double-conversion Version: 2.0.1-1~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 139 Depends: neurodebian-popularity-contest, libdouble-conversion1 (= 2.0.1-1~nd70+1) Multi-Arch: same Homepage: http://double-conversion.googlecode.com Priority: extra Section: debug Filename: pool/main/d/double-conversion/libdouble-conversion-dbg_2.0.1-1~nd70+1_sparc.deb Size: 89150 SHA256: af2d994a0ca31945c42d512cfef07dda68819deeb5e28dbad8e865a0981be2ae SHA1: d431eedfba9c05ed227ffd75e105bdd9d2aa1a6c MD5sum: 14aae0b520a1ca8b83f5116db432fbef Description: routines to convert IEEE floats to and from strings (debugging symbols) This library provides routines to convert IEEE single and double floats to and from string representations. It offers at lot of flexibility with respect to the conversion format: shortest, fixed, precision or exponential representation; decimal, octal or hexadecimal basis; control over number of digits, leading/trailing zeros and spaces. . The library consists of efficient conversion routines that have been extracted from the V8 JavaScript engine. The code has been refactored and improved so that it can be used more easily in other projects. . This package contains the detached debugging symbols of the library. Package: libdouble-conversion-dev Source: double-conversion Version: 2.0.1-1~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 229 Depends: neurodebian-popularity-contest, libdouble-conversion1 (= 2.0.1-1~nd70+1) Homepage: http://double-conversion.googlecode.com Priority: extra Section: libdevel Filename: pool/main/d/double-conversion/libdouble-conversion-dev_2.0.1-1~nd70+1_sparc.deb Size: 62610 SHA256: 526b034fac67d86359e7ab84344ae2f57f0e918b77050a90cd0f6c5d1b5005ed SHA1: 096f2c859d2c4be28c66640c2d5e10fec7eb863b MD5sum: 7a402da6d4a48d21ed3143c5e51f7082 Description: routines to convert IEEE floats to and from strings (development files) This library provides routines to convert IEEE single and double floats to and from string representations. It offers at lot of flexibility with respect to the conversion format: shortest, fixed, precision or exponential representation; decimal, octal or hexadecimal basis; control over number of digits, leading/trailing zeros and spaces. . The library consists of efficient conversion routines that have been extracted from the V8 JavaScript engine. The code has been refactored and improved so that it can be used more easily in other projects. . This package contains a static version of the library and development headers. Package: libdouble-conversion1 Source: double-conversion Version: 2.0.1-1~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 110 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.6), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Multi-Arch: same Homepage: http://double-conversion.googlecode.com Priority: extra Section: libs Filename: pool/main/d/double-conversion/libdouble-conversion1_2.0.1-1~nd70+1_sparc.deb Size: 39926 SHA256: 74281ca0be471f698df5f134f57289dd44253ab82aa3038155fa20e0b06b00fd SHA1: 15f89dba152ea58667e492b567b96f70fc2a6adb MD5sum: 68ba59c85c2abf9ecda8ccb0d9b41020 Description: routines to convert IEEE floats to and from strings This library provides routines to convert IEEE single and double floats to and from string representations. It offers at lot of flexibility with respect to the conversion format: shortest, fixed, precision or exponential representation; decimal, octal or hexadecimal basis; control over number of digits, leading/trailing zeros and spaces. . The library consists of efficient conversion routines that have been extracted from the V8 JavaScript engine. The code has been refactored and improved so that it can be used more easily in other projects. . This package contains a shared version of the library. 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: 1:0.5.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 591 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.5.3-1~nd70+1_all.deb Size: 125314 SHA256: c0dbeb0fa61eb81365dc6f4a14c6368f0ae2f800c10d3302e814381b227c68d7 SHA1: 50d172ed414072c52a35443c5610bc15b73b4bee MD5sum: 8c814d9eeeb7e8d158456cc9fffb1bf8 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: libgoogle-glog-doc Source: google-glog Version: 0.3.3-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30 Depends: neurodebian-popularity-contest Homepage: http://code.google.com/p/google-glog/ Priority: optional Section: doc Filename: pool/main/g/google-glog/libgoogle-glog-doc_0.3.3-2~nd70+1_all.deb Size: 14732 SHA256: 2fbceb6ae2102be2438e02e767a5c7b8c45fccd1a9bcb4695085b2b6b5628dff SHA1: 68ad48be2effa1bca2f0fc0879da5cfffe4c913c MD5sum: 313e64eb90ad1ced8dce64071e685060 Description: documentation of gloogle-glog This library provides logging APIs based on C++-style streams and various helper macros. . This package contains documentation files. 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: libmia-2.0-doc Source: mia Version: 2.0.13-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12253 Depends: neurodebian-popularity-contest, libjs-jquery Enhances: libmia-2.0-dev Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/libmia-2.0-doc_2.0.13-1~nd70+1_all.deb Size: 748032 SHA256: 8527b0bb4b3aa25c0cf0b7549c919d530721984768142b1c9cbe54bc501b2f60 SHA1: 9f0eb7c3aee4244a01b217c48fe20aa6e2f8b4ee MD5sum: bc5873ea130b7d109b727560d966df3a Description: library for 2D and 3D gray scale image processing, documentation libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the Doxygen generated API reference. Package: libmialm-doc Source: libmialm Version: 1.0.7-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 233 Depends: neurodebian-popularity-contest Suggests: devhelp Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/libm/libmialm/libmialm-doc_1.0.7-2~nd70+1_all.deb Size: 25632 SHA256: 53dfaf199e159648ef41671c3db6a6db19903b7ab23a5f0c3320deed90205c08 SHA1: 7d71dc5cbe43db379c6e192de9e11aff59904350 MD5sum: 443952d2baa93870673c8bf9dba721e9 Description: Documentation for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package contains the library documentation. Package: libnifti-doc Source: nifticlib Version: 2.0.0-2~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1537 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-2~nd70+1_all.deb Size: 252296 SHA256: 11a4a201e5e15312469bb279c5bc9cacafc822353fdbc14d760e8c98eec0d892 SHA1: 90350f4a2203772a84b66713b14600b032d5f2cc MD5sum: 7476594f01c396eb51e340187d187ef4 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libopenwalnut1-doc Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 43478 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.4.0~rc1+hg3a3147463ee2-1~nd70+1_all.deb Size: 5036628 SHA256: 9706d2dae1174bcb73c71fd2e55cd6e028953976d2ac3dedc08a0b89e510eb20 SHA1: 1108143f738996653844f935c3a678e96ea2913e MD5sum: 7643d3db48c92efd846dfd839d293b36 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: liborthancclient-doc Source: orthanc Version: 0.7.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 510 Depends: neurodebian-popularity-contest Homepage: https://code.google.com/p/orthanc/ Priority: optional Section: doc Filename: pool/main/o/orthanc/liborthancclient-doc_0.7.2-1~nd70+1_all.deb Size: 86952 SHA256: 3ae389d4aae2cb043286f7dec3e7e7062a7a8869df0c6fd5c0e18ae61b6c071c SHA1: 9e675411af8f126bf8f543e8966cfc8e0a7c2936 MD5sum: c939a250e91f44d048d300aa31a9ebcb Description: Orthanc Client documentation Orthanc Client is a library to access the content of a remote instance of Orthanc. Orthanc is a lightweight, RESTful DICOM server for healthcare and medical research. . This package includes the documentation and the sample codes. Package: libusb-1.0-doc Source: libusb-1.0 Version: 2:1.0.19-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1027 Depends: neurodebian-popularity-contest Conflicts: libusb-1.0-0-dev (<< 1.0.16) Replaces: libusb-1.0-0-dev (<< 1.0.16) Homepage: http://www.linux-usb.org/ Priority: optional Section: doc Filename: pool/main/libu/libusb-1.0/libusb-1.0-doc_1.0.19-1~nd70+1_all.deb Size: 169912 SHA256: 37d978b18cec514b1062bf2e70a5097bc4c5a0b02d8a3817aa25fe4ef8e9e1f1 SHA1: d3138ac53ff6cb795e9323ba861ff61872f32177 MD5sum: e97aee0b7b134da88ca705ca5884f0d0 Description: documentation for userspace USB programming Library for programming USB applications without the knowledge of Linux kernel internals. . This package contains the libusb 1.0 API reference manual in HTML format. 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: 0.0.21~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 39 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_0.0.21~nd70+1_all.deb Size: 7690 SHA256: f41deabbc97632c06372fd1f235e6e078e382c0a1aa64f8940d72688b0b3f294 SHA1: 734c5a8f3838d53e5dfe96de1736da3680cc9821 MD5sum: 914d38f498cd9dfb1b0f7bafd9889664 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: mia-tools-doc Source: mia Version: 2.0.13-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1140 Depends: neurodebian-popularity-contest Enhances: mia-tools Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/mia-tools-doc_2.0.13-1~nd70+1_all.deb Size: 79662 SHA256: 6392fa517b571c424994013005f9108c237351f18e2b64547c20e2e76b93fd9e SHA1: cd3ff76293659d5b20799f84e1ef618eef56ebab MD5sum: f19386bf836a60acfcac219f32d04a54 Description: Cross-referenced documentation of the MIA command line tools Cross referenced documentation of the command line tools and plug-ins that are provided by the MIA gray scale image processing tool chain. These lines tools to provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes interactively from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statistics over images. Package: mricron-data Source: mricron Version: 0.20140804.1~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1710 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20140804.1~dfsg.1-1~nd70+1_all.deb Size: 1665984 SHA256: d6052de157674400136cbdee21c83c526731797620cd0a53be019cf5daa974f6 SHA1: 703892b09c0d6430b6b9b35f450f81d5e2a76df2 MD5sum: 0d67d2e7ff88723243653afa12a4b0a2 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: 0.20140804.1~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1022 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20140804.1~dfsg.1-1~nd70+1_all.deb Size: 740790 SHA256: 6cef9b52694701bdf50300409c40d50a0e0eb52dab809f080c3ebb9cd73de8f9 SHA1: f82ba2d98a2241084fcc9be1d315fe3596ffa5bd MD5sum: 018b8ab025c7f4c5df919b6c6030dd17 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: mridefacer Version: 0.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 673 Depends: neurodebian-popularity-contest, fsl-5.0-core | fsl-core Homepage: https://github.com/hanke/mridefacer Priority: optional Section: science Filename: pool/main/m/mridefacer/mridefacer_0.1-1~nd70+1_all.deb Size: 637298 SHA256: ff89993ce0a47d1451e3d508b6a6300abc56e370211a4cc1ddf1d2700283dea4 SHA1: 693d6b7c6d5fe4b546b425a9d11cf2f45a07c9d2 MD5sum: cd79cc708a2e3383b094cb53ddc22d5c Description: de-identification of MRI data This tool creates a de-face mask for volumetric images by aligning a template mask to the input. Such a mask can be used to remove image data from the vicinity of the facial surface, the auricles, and teeth in order to prevent a possible identification of a person based on these features. mrideface can process individual or series of images. In the latter case, the computed transformation between template image and input image will be updated incrementally for the next image in the series. This feature is most suitable for processing images that have been recorded in temporal succession. Package: mrtrix-doc Source: mrtrix Version: 0.2.12-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3528 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.12-1~nd70+1_all.deb Size: 3323460 SHA256: d0ee577119384d1b54f950a6af309d70376785856583cbe54399de4098f8edb5 SHA1: 7b977b2a960b56ff708e1000453e1e1cd47c3188 MD5sum: 18943d52a05ce45055f7cbff15b8eb46 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: netselect-apt Source: netselect Version: 0.3.ds1-25~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd70+1_all.deb Size: 17856 SHA256: db383e91d00a8162f9d9263e141800c4a58a0b2223826869c1cba57a79b5588e SHA1: 4d96b2698446fb7e07aaf5752bad5a7b28521f11 MD5sum: bd2f9ed8074a39ec7b8f92f15b47d48d Description: speed tester for choosing a fast Debian mirror This package provides a utility that can choose the best Debian mirror by downloading the full mirror list and using netselect to find the fastest/closest one. . It can output a sources.list(5) file that can be used with package management tools such as apt or aptitude. Package: neurodebian Version: 0.37.4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 112 Depends: python, wget, neurodebian-archive-keyring, debconf (>= 0.5) | debconf-2.0 Recommends: netselect Suggests: neurodebian-desktop, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian_0.37.4~nd70+1_all.deb Size: 34532 SHA256: 90cac982caec4237641ec4d2694acee3e9c5007fd10a735476657e3717a2e8c0 SHA1: b39a53a89a7f4b909821aafd512e21cbc47c54e1 MD5sum: c28297bd728e9415cc598dee4bbcf0e5 Description: neuroscience-oriented distribution - repository configuration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package enables the NeuroDebian repository on top of a standard Debian or Ubuntu system. Package: neurodebian-archive-keyring Source: neurodebian Version: 0.37.4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 47 Breaks: neurodebian-keyring (<< 0.34~) Replaces: neurodebian-keyring (<< 0.34~) Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-archive-keyring_0.37.4~nd70+1_all.deb Size: 10498 SHA256: 182923976e8c8b9f0ee1cf966bb02d5770968a237c50b6d841e7472d35bb758e SHA1: 37988d1eab346d822257f42405951f040b9e324f MD5sum: d8f5fb18725edf7fea8764ebae595d14 Description: neuroscience-oriented distribution - GnuPG archive keys The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-desktop Source: neurodebian Version: 0.37.4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 223 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, adwaita-icon-theme | gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.37.4~nd70+1_all.deb Size: 119030 SHA256: a555396c9ed1137dacd4dbff0b2ddc9bc0a5af04a669ea31f7b574e696b9dcdd SHA1: e340e0de2451c433903cdc02c51a1104292f8f7c MD5sum: 26a967b8d7cdfd5a5854c275915d79d6 Description: neuroscience-oriented distribution - desktop integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring the most popular neuroscience tools, which will be automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.37.4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 139 Depends: devscripts, neurodebian-archive-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils, cowbuilder Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.37.4~nd70+1_all.deb Size: 36054 SHA256: ffcd36d92984d2636ac7c99af978bef6069bd2517c192a721466515de90b542e SHA1: f4ea386751cd95c64202800953a2bfe0a7f6faa6 MD5sum: 0160e22ca65a209ed2defcc8b7f7bc29 Description: neuroscience-oriented distribution - development tools The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.37.4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 195 Depends: virtualbox-guest-utils, virtualbox-guest-x11, virtualbox-guest-dkms, sudo, neurodebian-desktop, lightdm | x-display-manager, zenity Recommends: chromium, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.37.4~nd70+1_all.deb Size: 18314 SHA256: b97408cda73376b53f908110830b4d89f8e4bc2389635549faa877267c15e470 SHA1: d7fadc8e8a097255aaedf0bf8a451b1aa7e42ae4 MD5sum: a3817bc8088a28bb6df23efc968a0704 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: 0.32~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_0.32~nd70+1_all.deb Size: 7622 SHA256: 348ac2ebb354d9666b66e0bc5c796dbe18b77bce6af28cd3af7c96da52d1c114 SHA1: 4474f374987937f645a2d731986bb78f82c3644f MD5sum: b4c1e764964bf07f648c6a78e4d6a7ca 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: 0.37.4~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 50 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.37.4~nd70+1_all.deb Size: 12530 SHA256: de1620f2246719eb451596120f3da18e36c8272081130842592ff508ee7471cd SHA1: 94c856195f5f4c9e909d7ba6548b29c4af11542f MD5sum: ff51cac3d854fc4d3bce80eac9df2ba7 Description: neuroscience-oriented distribution - popcon integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . 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 (either Debian or Ubuntu) popcon server. . Participating in popcon is important for the following reasons: * Popular packages receive more attention from developers; bugs are fixed faster and updates are provided quicker. * It ensures that support is not dropped for a previous release of Debian or Ubuntu while there are active users. * User statistics may be useful for upstream research software developers seeking funding for continued development. . This requires that popcon is activated for the underlying distribution (Debian or Ubuntu), which can be achieved by running "dpkg-reconfigure popularity-contest" as root. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.11-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 732 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.11-1~nd70+1_all.deb Size: 616680 SHA256: f8ab8a599351d982abcaf1b319703973ac88042a8c0ed159be28556c38b4d97c SHA1: 1ed539ce98f925d2a658719d992a8852f194786e MD5sum: fb709121f380ea0a79f6c0a5dd6691d3 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: 0.5.20+ds-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2887 Depends: neurodebian-popularity-contest, g++-4.9 | g++-4.8 | g++-4.7 | 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), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.20+ds-1~nd70+1_all.deb Size: 712838 SHA256: 1e50ba8ae511a234f50d845e01a4229d233a0bf202b132fe290c06bdf03c3826 SHA1: 39acb5e9d79b92550d37252753acfef41d44e73f MD5sum: 52568a38027ea3cb92e67d5ad539f8e8 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: 0.27.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 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_0.27.4-2~nd70+1_all.deb Size: 25359168 SHA256: d4a3a47eb33a46f3ce6dfab7ff0c68cf8b4a50b524036a193dbcb74d37d66b78 SHA1: 314c22fe8ee10f62bfb07e5d4bcc767e0ca05062 MD5sum: 2cf1184acc7c84d31652bde5ecdc31f4 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: patool Version: 1.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 162 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: file Suggests: arj, bzip2 | lbzip2 | pbzip2, cabextract | lcab, ncompress, cpio | bsdcpio, lzop, p7zip-full, rar | unrar | unrar-nonfree, zip | unzip, rpm2cpio, binutils, lha, unace | unace-nonfree, arc | nomarch, unalz, lrzip (>= 0.551), tar (>= 1.26) | star | bsdtar, rzip, zoo, xdms, orange, lzip | plzip | clzip | pdlzip, sharutils, flac, shorten, unadf, archmage, genisoimage, python-argcomplete Homepage: http://wummel.github.io/patool/ Priority: optional Section: utils Filename: pool/main/p/patool/patool_1.7-1~nd70+1_all.deb Size: 33452 SHA256: e60ea509694f98fddc8e07452935a9067ee71d94d8921774791413c7b66a1cb1 SHA1: f0c73d2a9a743eee10a0a59b3d7ba0ea59f7cf49 MD5sum: 675b88890fd3df1241c7cfa4806c7d26 Description: command line archive file manager Various archive formats can be created, extracted, tested, listed, compared, searched and repacked by patool. The archive format is determined with file(1) and as a fallback by the archive file extension. . patool supports 7z (.7z), ACE (.ace), ADF (.adf), ALZIP (.alz), AR (.a), ARC (.arc), ARJ (.arj), BZIP2 (.bz2), CAB (.cab), compress (.Z), CPIO (.cpio), DEB (.deb), DMS (.dms), FLAC (.flac), GZIP (.gz), ISO (.iso), LZH (.lha, .lzh), LZIP (.lz), LZMA (.lzma), LZOP (.lzo), RAR (.rar), RPM (.rpm), RZIP (.rz), SHAR (.shar), SHN (.shn), TAR (.tar), XZ (.xz), ZIP (.zip, .jar) and ZOO (.zoo) formats. . It relies on helper applications to handle those archive formats (for example bzip2 for BZIP2 archives). . The archive formats TAR, ZIP, BZIP2 and GZIP are supported natively and do not require helper applications to be installed. Package: psychopy Version: 1.83.04.dfsg-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15993 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk3.0, python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-opencv, python-imaging, python-serial, python-pyo, python-psutil, python-gevent, python-msgpack, python-yaml, python-xlib, python-pandas, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid, libavbin0 Conflicts: libavbin0 (= 7-4+b1) Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.83.04.dfsg-2~nd70+1_all.deb Size: 9002868 SHA256: 4cc36daf7b7cbbd669c64b041dca430b4ba9057121e48fd32d3289680d12e172 SHA1: 4e781ed28f10225ea6bcef1ced0b2bc4b36926b5 MD5sum: 111c6e96f8649a104166e15b076c4409 Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.11.20140816.dfsg1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 58457 Depends: neurodebian-popularity-contest Recommends: subversion Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.11.20140816.dfsg1-1~nd70+1_all.deb Size: 24807576 SHA256: d039d632bb720f85cb1c582f41903a5c3ef59dcb27ae0f39fee1f8ccd9579695 SHA1: 53e0c9fada0f9d805538f48b063a00758efd191e MD5sum: 84699c4676de2245b6d2f37317db83c8 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-citeproc Source: citeproc-py Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 754 Depends: neurodebian-popularity-contest, python-lxml, python (>= 2.6.6-3), python (<< 2.8) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python-citeproc_0.3.0-1~nd70+1_all.deb Size: 105698 SHA256: 3968ddd09277d712a599913ccfdc14cb8f1b429e9a85ab1cb8f7a2c96cf9d66d SHA1: 826e267ee916367a238d8671fea48d7330279e85 MD5sum: 79ad51fa6f365ead69cbd205537c25d4 Description: Citation Style Language (CSL) processor for Python Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python modules. Package: python-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 67 Depends: neurodebian-popularity-contest, python (>= 2.6.6-3), python (<< 2.8) Homepage: http://contextlib2.readthedocs.org/ Priority: optional Section: python Filename: pool/main/c/contextlib2/python-contextlib2_0.4.0-3~nd70+1_all.deb Size: 9430 SHA256: 8d33322efbe1d55462415d6cba0405e95ea2c05b6f39e85d361428bac216a618 SHA1: 563616421704be5d0146af3184690de9601604fa MD5sum: 99b34fe3f6e50deea68190ea9a38db4d Description: Backport and enhancements for the contextlib module - Python 2.7 contextlib2 is a backport of the standard library's contextlib module to earlier Python versions. . It also serves as a real world proving ground for possible future enhancements to the standard library version. . This package contains the Python 2.7 module. Package: python-dicom Source: pydicom Version: 0.9.9-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1583 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://pydicom.org/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.9-1~nd70+1_all.deb Size: 434126 SHA256: c51abf6dec4298b7bf24bf20d5c37add7c78ed8f0f57fd7e3f285a4d8a4fd7dd SHA1: 2bf2dc94e841547a572a4df8f235895d84093507 MD5sum: fabba1c9b3061334b0a3cda582034cd4 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: 0.9.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4663 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-dipy-lib (>= 0.9.2-1~nd70+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy_0.9.2-1~nd70+1_all.deb Size: 2504204 SHA256: 5b9f78c06ac4aba77da059e734646bb13c1a5dba3a8797fdbce59cdeb8497e7a SHA1: add4f1b0fe4e743080840beb2ac13ceb75472eda MD5sum: 2ae607765dbd0304fc4993189f2465ca 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: 2.7 Package: python-dipy-doc Source: dipy Version: 0.9.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12482 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: optional Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.9.2-1~nd70+1_all.deb Size: 11078680 SHA256: 45cb1fd7627c17de59e40ac83436dd320b56693543ddb9439deab84447ca5378 SHA1: df1601cd477f5798aa5d17f50ea8e34f6a1f1a95 MD5sum: 49b0e087422282c5ef0deff099d1780b 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-expyriment Version: 0.7.0+git34-g55a4e7e-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2443 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-support (>= 0.90.0), python-pygame (>= 1.9.1~), python-opengl (>= 3.0.0), ttf-freefont, libjs-jquery, libjs-underscore Recommends: python-serial (>= 2.5~), python-numpy (>= 1.3.0~) Suggests: python-parallel (>= 0.2), python-pyxid Homepage: http://www.expyriment.org Priority: optional Section: science Filename: pool/main/p/python-expyriment/python-expyriment_0.7.0+git34-g55a4e7e-3~nd70+1_all.deb Size: 841718 SHA256: 84b05bab270dfe59e32432bfe9e62e956575f65f3711092048a51f44fa11e310 SHA1: ed3e5fcd48c3c02c5eb9223076a3b8b67dad8109 MD5sum: 6b9e610629e9e6dc2b78408e1e093beb Description: Python library for cognitive and neuroscientific experiments Expyriment is a light-weight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments. The major goal is to provide a well-structured Python library for a script-based experiment development with a high priority on the readability of the resulting programme code. Due to the availability of an Android runtime environment, Expyriment is also suitable for the development of experiments running on tablet PCs or smart-phones. Package: python-future Version: 0.15.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2149 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, python (>= 2.7) | python-argparse, python-importlib, python (>= 2.6.6-3), python2.7, python (<< 2.8) Suggests: python-future-doc Homepage: https://python-future.org Priority: optional Section: python Filename: pool/main/p/python-future/python-future_0.15.2-1~nd70+1_all.deb Size: 430246 SHA256: 8cb241f594de609f64ce4dcbd7c6de276f403ea510095763f06a64f141f4eaa4 SHA1: 2db5485755219edae09dd9a069f109cefea6ad52 MD5sum: 4ac3becdefa27ca32c99be67813b8d72 Description: single-source support for Python 3 and 2 - Python 2.x Future is the missing compatibility layer between Python 2 and Python 3. It allows one to use a single, clean Python 3.x-compatible codebase to support both Python 2 and Python 3 with minimal overhead. . The imports have no effect on Python 3. On Python 2, they shadow the corresponding builtins, which normally have different semantics on Python 3 versus 2, to provide their Python 3 semantics. . This package contains the Python 2.x module. Package: python-future-doc Source: python-future Version: 0.15.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1470 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://python-future.org Priority: optional Section: doc Filename: pool/main/p/python-future/python-future-doc_0.15.2-1~nd70+1_all.deb Size: 386416 SHA256: 9898a2ab790b5ddc84dd41412ea65ebc26ee9805963bf0442bf566b7ccb79603 SHA1: 29f315b38da52dea7e61bfac6032206dcbfba79f MD5sum: 29a61873d5cde71f8c77ef85386985a7 Description: Clean single-source support for Python 3 and 2 - doc Future is the missing compatibility layer between Python 2 and Python 3. It allows one to use a single, clean Python 3.x-compatible codebase to support both Python 2 and Python 3 with minimal overhead. . The imports have no effect on Python 3. On Python 2, they shadow the corresponding builtins, which normally have different semantics on Python 3 versus 2, to provide their Python 3 semantics. . This package contains the documentation. Package: python-git Version: 1.0.1+git137-gc8b8379-1+nd1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1628 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-gitdb (>= 0.6.4), git (>= 1:1.7) | git-core (>= 1:1.5.3.7), libjs-jquery Suggests: python-smmap Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python-git_1.0.1+git137-gc8b8379-1+nd1~nd70+1_all.deb Size: 409868 SHA256: 06d7b2a9947b61897f508f8f545f832fcb4a1fb08c730caddc04b2dbefa22631 SHA1: 35456e59f415d569b192b0e35c800e7ef6d4f64d MD5sum: 8ebffea5827af708c77801c2150ac39a Description: Python library to interact with Git repositories python-git provides object model access to a Git repository, so Python can be used to manipulate it. Repository objects can be opened or created, which can then be traversed to find parent commit(s), trees, blobs, etc. Python-Version: 2.7 Package: python-humanize Version: 0.5.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 158 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Homepage: http://github.com/jmoiron/humanize Priority: optional Section: python Filename: pool/main/p/python-humanize/python-humanize_0.5.1-2~nd70+1_all.deb Size: 15924 SHA256: 0e5d8a165c78481653c5b89606d5aaec0e7b5737fec72c42b69cd41334b72fff SHA1: ac76e3f605f1c0a0f4ce2c9a9b80f6cecf4ffefc MD5sum: 0ee2859b8df7e1ee3cf927ff5087209f Description: Python Humanize library (Python 2) This library proposes various common humanization utilities, like turning a number into a fuzzy human readable duration ('3 minutes ago') or into a human readable size or throughput. . This is the Python 2 version of the package. Package: python-jdcal Source: jdcal Version: 1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python-jdcal_1.0-1~nd70+1_all.deb Size: 8348 SHA256: da8ea685906de9fc1ef1b8e03c49e5c9616a69121f094ca951962ee4dccbc8e2 SHA1: 952b8f6c71d382846c596dcfe81e0876290e4288 MD5sum: d1fb40edfb0623f6fb312b1191eec462 Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python-joblib Source: joblib Version: 0.8.4-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 275 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.8.4-1~nd70+1_all.deb Size: 76760 SHA256: 4e8b7bbbfa5fc5cc2cad078c9e61c2f173e0886eaa917da8411a13e4c9648699 SHA1: 047158f64b9c646a4df039cd4ff3755c1eaea63c MD5sum: f340138ccc2493d2f687f2bece4edcdc 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. . This package contains the Python 2 version. 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+git19-g4ec2f29-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1553 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git19-g4ec2f29-1~nd70+1_all.deb Size: 501106 SHA256: 59b5767f5015a0f7d5982db6e0d6e3c13cd1c6a97ba5a5f800ddbbfefcfdec40 SHA1: 825ec52f638b53ca984e99510fb99756a30d24b1 MD5sum: 1e9db109b66789708b2bb7ee870e7c8b 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-mne Version: 0.10.1+dfsg-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9492 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.6-mne, python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.10.1+dfsg-1~nd70+1_all.deb Size: 4797094 SHA256: 8bf5f37db1c6ceb2be1fe173bb0f78cb7b23f1f5b9eb7a67853c959ba0cb4983 SHA1: 0ba1642327d0db2e1bff5ea85e962a86da3f6092 MD5sum: 98f17c598f7bff80d0482c20d97adb0c Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py-doc Source: mpi4py Version: 1.3.1+hg20131106-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 256 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd70+1_all.deb Size: 73296 SHA256: 23cfd5ae13016514e28312e197117270c06838479f8cb616d7a8618f81b5a500 SHA1: bb8a201cac4ec1a465fa2a76b2d8e06458941208 MD5sum: 8f4b56dde1e544c71a843c43b944e2d4 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: 2.4.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8643 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.4.3-1~nd70+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-nipy, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, python-shogun, liblapack-dev, python-pprocess, python-statsmodels, python-joblib, python-duecredit Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.4.3-1~nd70+1_all.deb Size: 5314986 SHA256: 36b4fe3512bb66f06e0f74ba840817f866b637b6ab5811b80c8c2493acb264dc SHA1: c0bb472303aa1373a457adcbcdc250aeeb970d46 MD5sum: c64287a233ca29fcd6d1aa0469a624dd 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: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.4.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29738 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_2.4.3-1~nd70+1_all.deb Size: 7401838 SHA256: 6c34a0c2fc2a366d5b428fdecf4fa9d184a5779d78c7acf551954838dc67003b SHA1: 31ec65de3f17ef62cd603887a39d11f05e54fda5 MD5sum: 0355a7d41b60a4fb2b6f8a948a0c2ecc 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: 0.3.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2935 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_0.3.3-1~nd70+1_all.deb Size: 1502424 SHA256: 8a6e0b290d15ba1061c397e29f6d39948afda5bf8d2e0e232f6cef4729b77dd3 SHA1: f82c30ca58b1659395e216ced99521c6f38df351 MD5sum: 5ee645804ad02f06ef1c6693e0353251 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: 2.0.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63504 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python2.6, python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.0.2-1~nd70+1_all.deb Size: 2392368 SHA256: bed1adc272b96e9df97195ed290250c72f5c6d39961f672b3437eed54faee1f2 SHA1: 00c29a5f0dc5a1c1527ea9a8413baa79e08806c6 MD5sum: 3816d76964e43a83425812c7488d4184 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 Package: python-nibabel-doc Source: nibabel Version: 2.0.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5561 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-mathjax Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_2.0.2-1~nd70+1_all.deb Size: 3086694 SHA256: 88a7aaf890a045fd89354a2ec2a4379f03744757b05c4dd47413ce3ddee2e036 SHA1: 8cb705eec397aada137d17644e1154f39ef10e45 MD5sum: 9ac89c6242e9a9825c4e45ff03df3a10 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+git262-gbb838d7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3166 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.6.6-7~), python (<< 2.8), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0+git262-gbb838d7-1~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+git262-gbb838d7-1~nd70+1_all.deb Size: 904218 SHA256: 94a57ccc67b7d84076890e6cf15c493e1ceddb7697159fd7dd3fe317c91f0879 SHA1: d33059568ded44dbe780387b717c3b9e0ebd21b7 MD5sum: 5959745aa53fe1bfb448734e70a2b635 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+git262-gbb838d7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10257 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.3.0+git262-gbb838d7-1~nd70+1_all.deb Size: 3856268 SHA256: ce05de5f92ac99480fd10615e19792a3dde7aa3a0f3ede1f2de7a5634fb5041d SHA1: 729c9b6cfea429c154c02a67aeea14115b30b482 MD5sum: 59f87311c3907033deea7869d9ba34b6 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-nipy-lib Source: nipy Version: 0.3.0-1~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 2613 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.6), liblapack3 | liblapack.so.3 | libatlas3-base, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.6-nipy-lib, python2.7-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.3.0-1~nd70+1_sparc.deb Size: 918274 SHA256: 68ebfa2684d2a75fdb4191f64ec96ee573a623e96ba2de2af99ea465951c8daa SHA1: bfb92b469bf3b172d15a228ecda3e795a26d68b6 MD5sum: f29313c211b09701313b71ae66addf35 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides architecture-dependent builds of the libraries. Python-Version: 2.6, 2.7 Package: python-nipy-lib-dbg Source: nipy Version: 0.3.0-1~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 4864 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3 | libatlas3-base, libc6 (>= 2.6), liblapack3 | liblapack.so.3 | libatlas3-base, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), python-nipy-lib (= 0.3.0-1~nd70+1) Provides: python2.6-nipy-lib-dbg, python2.7-nipy-lib-dbg Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg_0.3.0-1~nd70+1_sparc.deb Size: 1006694 SHA256: eeabc0792790b4e70001c1886907ce3e3e587080efacc1ed96d47ecdc37b89ff SHA1: ddcc6979aa0059e179421957a7ecb906f1cadd47 MD5sum: bcc894be8606e02df5aee02fe1cbb5f7 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides debugging symbols for architecture-dependent builds of the libraries. Python-Version: 2.6, 2.7 Package: python-nipype Source: nipype Version: 0.10.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4884 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), 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, mne-python, elastix, ants Provides: python2.6-nipype, python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.10.0-1~nd70+1_all.deb Size: 1410670 SHA256: ddde92e42d320ad723d5c51946be6bc45df26288c7aa4263d66db21abb07b431 SHA1: 802f3a5ced7d553b4e0ac3d95d754401a04c6022 MD5sum: 8775c298d91c76cf7bd1cab209d42aa0 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: 0.10.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20344 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.10.0-1~nd70+1_all.deb Size: 10548596 SHA256: 2bc2b7c66f45dc1ee73c5d412eecd9d1bceb20f0074b71965e89e74387c1dc48 SHA1: 9a81a39b14874a1a530cda5479642f01fca5caab MD5sum: 51a2db48e05db26d9cb94957e861edcf 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: 0.6-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9487 Depends: neurodebian-popularity-contest, python (>= 2.6.6-3), python (<< 2.8), 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_0.6-1~nd70+1_all.deb Size: 3931038 SHA256: 8065f37bdf0510c5c75d8d8c510d5f93df05e71ca4701dff3d107e818e3b31ac SHA1: 99a3452421e8a8017c1e6a0be65a907c0c2956d3 MD5sum: 91be1c73e4c6e3920b64cd018d72bad7 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: 0.6-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7698 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.6-1~nd70+1_all.deb Size: 6063756 SHA256: 9c3efb078f5edcd96bb19a656edce7c517437c5b66b38ebf2948e1e22a8f801e SHA1: b78160675405b250781c84a9fb32c41749366a08 MD5sum: b687b449cf515fac3a8117f5c9d43f50 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: 1.7.0+ds1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 453 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0) Recommends: python-nose, python-pil, python-imaging Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.7.0+ds1-1~nd70+1_all.deb Size: 92736 SHA256: 0be52cec70c06585ee53c9f160c3239676bb2ddf5f357afaf46cae9b7e134d88 SHA1: 75554545c097b272398af1be0e2549ba120f7c54 MD5sum: ba95b41b529c58a1e01bb77b74ea534c 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-ordereddict Source: ordereddict Version: 1.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~) Provides: python2.6-ordereddict Homepage: http://pypi.python.org/pypi/ordereddict Priority: optional Section: python Filename: pool/main/o/ordereddict/python-ordereddict_1.1-1~nd70+1_all.deb Size: 3952 SHA256: 3ff583bb0a490def26a417dd91df57e567f0e50fa5c4dcf1fbac845b18971ddd SHA1: 10bfe94590ba8a1dda88cc2c3070d50b79be35b0 MD5sum: e0bbf41a9314c3f50dda3c57f4d2e3c2 Description: big-oh performance that matches regular dictionaries OrderedDict is the recipe has big-oh performance that matches regular dictionaries (amortized O(1) insertion/deletion/lookup and O(n) iteration/repr/copy/equality_testing). OrderedDict that works in Python 2.4-2.6. Package: python-pandas Source: pandas Version: 0.14.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8987 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.6~), python-pandas-lib (>= 0.14.1-1~nd70+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.14.1-1~nd70+1_all.deb Size: 1666494 SHA256: be035dcbcbbd63810eac73063065584081e92bbb7997c4d57d6ce31d299a6670 SHA1: ee7d3ce3127e95983c32db6cc0f2c45e17c6962d MD5sum: 8a9a2a18bcd2221c77b84f57ec2ee562 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-pandas-lib Source: pandas Version: 0.13.1-2~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 4329 Depends: neurodebian-popularity-contest, libc6 (>= 2.6), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.6.1), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Provides: python2.7-pandas-lib Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas-lib_0.13.1-2~nd70+1_sparc.deb Size: 1452044 SHA256: a4f646a1599d1d8341a0a8a263ee41c0031fb9fb87f1377676910a4bf39d3ceb SHA1: 74556b42b0cb370136498dda38535145e484c86c MD5sum: 51ef577e61eaff463f16e3dd0de4db8f Description: low-level implementations and bindings for pandas This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 2 version. Python-Version: 2.7 Package: python-patsy Source: patsy Version: 0.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 827 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), python-six, python-numpy Recommends: python-pandas, python-openpyxl Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.4.1-1~nd70+1_all.deb Size: 226244 SHA256: b7cec7368dde23a92dd024f1c436bbb7246b777f85b6d58bf80ec73d0da52890 SHA1: 8cf6813c00fae59ffa82b02dfc71d2e23011b784 MD5sum: 1024f87f134f51bcaa0651687a734452 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1306 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.4.1-1~nd70+1_all.deb Size: 565352 SHA256: 56609993a0de0b18e3adcf227acf2b465391ec828b562c74b5f573677a01fc3a SHA1: f88c454967e2f2e0f390ac39be1b8c0db3565ebe MD5sum: 4112017150bef3c594856a0d485520ce Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. 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+nd0~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 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+nd0~nd70+1_all.deb Size: 108524 SHA256: 365c53d41b61407752dc5e0b44d46dde2cf8b6a79959506d22b5284c3b84386c SHA1: 5c659f598ab0287d487e665498c930252c2b7c5b MD5sum: 3bb37649f155ba66381ecd5a1d60cfa4 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-py Version: 1.4.30-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 282 Depends: neurodebian-popularity-contest, python (>= 2.6.6-3), python (<< 2.8), python-pkg-resources Suggests: subversion, python-pytest, python-pytest-xdist Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python-py_1.4.30-1~nd70+1_all.deb Size: 78518 SHA256: d837bce3fffea2c17a9008060fa43875e7d3ec81ae7bf8a74a65826c982070e9 SHA1: 8c8b268aa93282740de764cce5953501173fb932 MD5sum: faad94c179d18166fe86e06e58fb0ccf Description: Advanced Python development support library (Python 2) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 2 modules. 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-g365f8e3-2~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-g365f8e3-2~nd70+1_all.deb Size: 818246 SHA256: e75f811ed7831fb6d98f234c78dc1c7f238d96b8316e66c35703156f8c83aadf SHA1: eaa324f7ed77db6a1388c5e25e6cd5290f75f33d MD5sum: 051b80b1ebeb8634798c114172ef8638 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: 0.16.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 51 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_0.16.1-2~nd70+1_all.deb Size: 48292 SHA256: 33b215caa3cdb459a4e715f574b96e90f6c011e9bd9852a5ad5f45fc08ad4b6b SHA1: 8fc410c4f8a2d86552b494ae35e14f17a263b179 MD5sum: e054964ba8035b57c516841bfbfedc58 Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits.statsmodels Source: statsmodels Version: 0.5.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.5.0-1~nd70+1_all.deb Size: 5622 SHA256: aced9ca8fc74948bddce3d3e4fa88a02399e8def6e504a27cebbb30ee20c9cbe SHA1: 9727dbe8d94182dacbf491e3523cd0cea4b4c5a0 MD5sum: a0f89341a8a53f73562b6963e703829a Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-seaborn Source: seaborn Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 686 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-pandas, python-matplotlib Recommends: python-statsmodels, python-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python-seaborn_0.6.0-1~nd70+1_all.deb Size: 144558 SHA256: af4f5087a55271cf24a46a245225cceb66ff0832f8b7a5d9154311759a898c10 SHA1: 907cd726ed156fa93301baaab4d1475e82f805c0 MD5sum: c45764f8a0bfb15c0d8bd2b5bad07033 Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 2 version of the package. 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-six Source: six Version: 1.8.0-1~bpo70+1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 94 Depends: neurodebian-popularity-contest, python (>= 2.6.6-3), python (<< 2.8) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python-six_1.8.0-1~bpo70+1~nd70+1_all.deb Size: 14696 SHA256: 85a096bc08b78353f0e48fdc3e9530dc31f71a44c3e1a0f01f18ccd7b407a355 SHA1: e8f04d36980afc332b0e0f4ae9e3932c98d98e8c MD5sum: 8035179f515d89a0f9160e565cfad726 Description: Python 2 and 3 compatibility library (Python 2 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 2 module path. It is complemented by python3-six. 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: 0.16.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4795 Depends: neurodebian-popularity-contest, python (>= 2.6.6-3), python (<< 2.8), python-numpy, python-scipy, python-sklearn-lib (>= 0.16.1-2~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_0.16.1-2~nd70+1_all.deb Size: 1359418 SHA256: 19c182857444f9fe155d94195ea9c25154300e8dca87ca82c1e3e8808bc3210d SHA1: 2f0a9fcee15dd21a30c4dafde236f235564a1c6d MD5sum: 967c8a7a9dd8ca2e4cb12213c8f78561 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: 0.16.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23446 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_0.16.1-2~nd70+1_all.deb Size: 5746322 SHA256: 9e9a509129fe0df15c8f9b113f8c70c21b454f7e82e102ac83c97bf7e55bf5ba SHA1: 43acb0286ee7b6f0af117942738f6c7739bc954f MD5sum: b48bac5df403e032e2ff72667d2b7ef6 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-smmap Version: 0.9.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 71 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8) Suggests: python-nose Provides: python2.6-smmap, python2.7-smmap Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python-smmap_0.9.0-1~nd70+1_all.deb Size: 22572 SHA256: 09e7c68b4d8ab6d4201fc9481db5e33523fadda79f05fb7013ba1d27e0597e6a SHA1: 6409d1036c76863bf1b7fdc08670f98bc5e8b854 MD5sum: 9e3699860d6b62221e7fc79b3dc51cf4 Description: pure Python implementation of a sliding window memory map manager Smmap wraps an interface around mmap and tracks the mapped files as well as the amount of clients who use it. If the system runs out of resources, or if a memory limit is reached, it will automatically unload unused maps to allow continued operation. 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-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 874 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python (>= 2.7), python (<< 2.8) Recommends: python-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python-sphinx-rtd-theme_0.1.8-1~nd70+1_all.deb Size: 420206 SHA256: 6cfce4ae32123360637185ec78c09578788ed2e0885bf1c6aef95f34491c83e9 SHA1: b86cb05537331ca6d2035b523b1d2ec0e63323fe MD5sum: 3772e10a1393efc6d2ee8915581717f2 Description: sphinx theme from readthedocs.org (Python 2) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 2 version of the package. Package: python-spyderlib Source: spyder Version: 2.2.5+dfsg-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4106 Depends: neurodebian-popularity-contest, python (>= 2.6.6-7~), python (<< 2.8), libjs-sphinxdoc (>= 1.0), libjs-jquery, libjs-mathjax, python-qt4 Recommends: ipython-qtconsole, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-numpy, python-psutil (>= 0.3.0), python-rope, python-scipy, python-sphinx Suggests: tortoisehg, gitk Breaks: spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.6-spyderlib, python2.7-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.2.5+dfsg-1~nd70+1_all.deb Size: 1877078 SHA256: c68eb60a149c6a838ec9fc509eeb4b98657dd2df46997afb9a9594a001163929 SHA1: f55ea33d3c104c0fcaf457d2699cfeabfada76f4 MD5sum: fe747979f715bb209d47b4f5681c5571 Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-spykeutils Source: spykeutils Version: 0.4.3-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2117 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_0.4.3-1~nd70+1_all.deb Size: 407372 SHA256: a33ea754249038ef08a3ce9d4c4399b018b539cdaa611b0d7a220e7a05993291 SHA1: b5cd70c9ca78969ca71b650c4b6f4c18fe3a7ecd MD5sum: a0694656192237d901d05290048b4438 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: 0.5.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20309 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.5.0-1~nd70+1), python-patsy 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: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.5.0-1~nd70+1_all.deb Size: 4669242 SHA256: 5e8a0bf317654408b38a6f568052ea60bbef40a69e1ed5f155a779e0cfe81e90 SHA1: a7d322d6197262cefc2a28ea26092b1ff6be048e MD5sum: e90c2216b3d0959c1a028e71084c8683 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: 0.5.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29891 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_0.5.0-1~nd70+1_all.deb Size: 7076508 SHA256: d1f7a1276f9462a71a4f641b6f61351787acac117b1c60f618d0b4a446d70915 SHA1: 922be3a869fbbf826d6da7b61277f0afc4dd4108 MD5sum: 74f6a3c21cd7be2438a1eb31c5d4f255 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-statsmodels-lib Source: statsmodels Version: 0.5.0-1~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 436 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.6.1), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.6) Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels-lib_0.5.0-1~nd70+1_sparc.deb Size: 79216 SHA256: 0a0c0b87868fa4ae710ab00c5dc213b716923a979e3f4a32f00319542c84423c SHA1: f01be788d5389907d7ba88c49678b5d22d7f9ef7 MD5sum: 518c3872691730f48511346519ff38ef Description: low-level implementations and bindings for statsmodels This package contains architecture dependent extensions 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: python-w3lib Version: 1.11.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41 Depends: neurodebian-popularity-contest, python (>= 2.7), python-six (>= 1.6.1), python (<< 2.8) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python-w3lib_1.11.0-1~nd70+1_all.deb Size: 15404 SHA256: c4bc028f0a6d04b9a46d3e339d22c90f407aac1807f2ec6faa1c8a03e5eb637a SHA1: 1067a91aa1180df1f27249431007dc594635d2b6 MD5sum: 12f69a3f5347d40b9872571e5cd5e653 Description: Collection of web-related functions for Python (Python 2) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 2 version of the package. Package: python3-citeproc Source: citeproc-py Version: 0.3.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 731 Depends: neurodebian-popularity-contest, python3-lxml, python3 (>= 3.2.3-3~) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python3-citeproc_0.3.0-1~nd70+1_all.deb Size: 100194 SHA256: a11abe60616dca3775afe584697028e71b2ffe7f4174838e5f6ad58b36ecf934 SHA1: 7551cbfd4a28ddc18470e0c9ff2eba2befd53a2d MD5sum: d44443de45859c09cddee8bfcdb2d2ec Description: Citation Style Language (CSL) processor for Python3 Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python 3 modules and the CLI tool csl_unsorted. Package: python3-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 55 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~) Homepage: http://contextlib2.readthedocs.org/ Priority: optional Section: python Filename: pool/main/c/contextlib2/python3-contextlib2_0.4.0-3~nd70+1_all.deb Size: 9206 SHA256: 8aa17d9297a6c42407ada617fc543f80d0ec8fdb3fbc0e2bf412b57e94df7249 SHA1: 6c5662a08f1e20e4eb6c3c195b7c1ffe3f24f2ad MD5sum: 1ae96a8bd925a164ec1038d36809d2a7 Description: Backport and enhancements for the contextlib module - Python 3.x contextlib2 is a backport of the standard library's contextlib module to earlier Python versions. . It also serves as a real world proving ground for possible future enhancements to the standard library version. . This package contains the Python 3.x module. Package: python3-future Source: python-future Version: 0.15.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1663 Pre-Depends: dpkg (>= 1.15.6~) Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3.2 Suggests: python-future-doc Homepage: https://python-future.org Priority: optional Section: python Filename: pool/main/p/python-future/python3-future_0.15.2-1~nd70+1_all.deb Size: 414730 SHA256: 61799689907b299825a057073a62c642ff9ffca2f373109beb045a1a3d632384 SHA1: 1e95ec8d0e4a2f91cad2dbb2b6f7d2ab4331bce9 MD5sum: 6b60038675fb2473c47fcc8dc13fcae5 Description: Clean single-source support for Python 3 and 2 - Python 3.x Future is the missing compatibility layer between Python 2 and Python 3. It allows one to use a single, clean Python 3.x-compatible codebase to support both Python 2 and Python 3 with minimal overhead. . The imports have no effect on Python 3. On Python 2, they shadow the corresponding builtins, which normally have different semantics on Python 3 versus 2, to provide their Python 3 semantics. . This package contains the Python 3.x module. Package: python3-humanize Source: python-humanize Version: 0.5.1-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 114 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~) Homepage: http://github.com/jmoiron/humanize Priority: optional Section: python Filename: pool/main/p/python-humanize/python3-humanize_0.5.1-2~nd70+1_all.deb Size: 14230 SHA256: 802cae58085919690f244c2b002dc6128e3e153b3b616a9052de742a78485a90 SHA1: 1921d412e6b4abdb2377ca04f29939000d380b1f MD5sum: 79d29249e95f0b1b5b5dbec47f75db7b Description: Python Humanize library (Python 3) This library proposes various common humanization utilities, like turning a number into a fuzzy human readable duration ('3 minutes ago') or into a human readable size or throughput. . This is the Python 3 version of the package. Package: python3-jdcal Source: jdcal Version: 1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python3-jdcal_1.0-1~nd70+1_all.deb Size: 7824 SHA256: 5bdfff822ef6095bc30f5e76758383e0618bad3e5508df67dcd01fcd159279f4 SHA1: d9f536955c46dad67c027ee9ddfde648141a72a4 MD5sum: 11511c0a1f0275aa7786e081b6400b3c Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python3-joblib Source: joblib Version: 0.8.4-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 252 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~) Recommends: python3-numpy, python3-nose, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.8.4-1~nd70+1_all.deb Size: 71652 SHA256: bbf364d28dab9165d64f37d99a23641605e9512f73d96b9e3371ca73a0d9fb3c SHA1: 48d6edc53fe21796317768c747f29421f4366f86 MD5sum: 74e90fd0c96b1e3ac2e7286b9cc47970 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. . This package contains the Python 3 version. Package: python3-nibabel Source: nibabel Version: 2.0.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63280 Depends: neurodebian-popularity-contest, python3-numpy, python3-scipy Suggests: python-nibabel-doc, python3-dicom, python3-fuse Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.0.2-1~nd70+1_all.deb Size: 2371322 SHA256: e24ccbeb4f838fb308de8a19af9ea11ebd8bdd369c83f58c9a292a61579c8371 SHA1: fe9268feaa33fb614d56d689a72d2ff66054a2d9 MD5sum: 89ba158f3d639745169f99c1cc9c1883 Description: Python3 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. Package: python3-pandas Source: pandas Version: 0.14.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8918 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-dateutil, python3-tz, python3-numpy (>= 1:1.6~), python3-pandas-lib (>= 0.14.1-1~nd70+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.14.1-1~nd70+1_all.deb Size: 1660916 SHA256: e3deb0e300bdc36e9c14edff51f90c7c447ba1adbb9ae95111732561499c98e5 SHA1: 34bff3e467ea08fc5b1c70701f03da30b04e058c MD5sum: cb66079236f59bd830bbab1f586b3105 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-pandas-lib Source: pandas Version: 0.13.1-2~nd70+1 Architecture: sparc Maintainer: NeuroDebian Maintainers Installed-Size: 4276 Depends: neurodebian-popularity-contest, libc6 (>= 2.6), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python3-numpy (>= 1:1.6.1), python3-numpy-abi9, python3 (>= 3.2), python3 (<< 3.3) Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas-lib_0.13.1-2~nd70+1_sparc.deb Size: 1418954 SHA256: 38318b82c67db7e254598071c573c97a4174ef71e07f6528a8803a0f41e96f6a SHA1: f509d1a39af7c50c4903d1c175189194b9cb3617 MD5sum: ddccb477a8002ea07b97aa2923a59b49 Description: low-level implementations and bindings for pandas - Python 3 This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 795 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-six, python3-numpy Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.4.1-1~nd70+1_all.deb Size: 224808 SHA256: bf36233d74c3dc93b5f794e1894a6967640096a3b8f4cd1ee97a07af9d4da41d SHA1: 56b79c902c261d00c0146e53acf671007e21c4f6 MD5sum: 8bcc89d3d66f01e3573667152386bb45 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-py Source: python-py Version: 1.4.30-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 269 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~), python3-pkg-resources Suggests: subversion, python3-pytest Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python3-py_1.4.30-1~nd70+1_all.deb Size: 76620 SHA256: a74e4468475c5f0bdf1a51714d99807f5f879d57735116a5009d04f083c43ceb SHA1: f9d34fd617c836286fb8eaf93e554f6cf040f94d MD5sum: 11294264841fbaaf65482c3ba36db38c Description: Advanced Python development support library (Python 3) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 3 modules. Package: python3-seaborn Source: seaborn Version: 0.6.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 676 Depends: neurodebian-popularity-contest, python3-matplotlib, python3 (>= 3.2.3-3~), python3-numpy, python3-scipy, python3-pandas Recommends: python3-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python3-seaborn_0.6.0-1~nd70+1_all.deb Size: 143806 SHA256: 942523cf83722ee8ec3accd3c1aec4a805bfa55ea93676c2f384c7b48f4a69da SHA1: 0f422660d6ae4fcd41fe0b6846fe03ab4fd37240 MD5sum: 8f9b74b27dbb5adf43fa38adef18d59d Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 3 version of the package. Package: python3-six Source: six Version: 1.8.0-1~bpo70+1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 74 Depends: neurodebian-popularity-contest, python3 (>= 3.2.3-3~) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python3-six_1.8.0-1~bpo70+1~nd70+1_all.deb Size: 13904 SHA256: c8d8b91b4850a797b976b16b009366e605939fc3af9fcec65f34ec1ecaca8c64 SHA1: 3894f1a468f505895b479473901378f9468757de MD5sum: 4bb2eef11fa797628724b6b9d5ef210e Description: Python 2 and 3 compatibility library (Python 3 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 3 module path. It is complemented by python-six. Package: python3-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python3 (>= 3.2.3-3~) Recommends: python3-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python3-sphinx-rtd-theme_0.1.8-1~nd70+1_all.deb Size: 145052 SHA256: 43bb2696de87bbbfeba92a081d6d4e8140f5171d54747e183520cbe6bbf112b0 SHA1: c97f0a51dd909a2d425fc580d3d7b6537c46d355 MD5sum: 024cd5a3a245d9bb758779b9c7ea66e8 Description: sphinx theme from readthedocs.org (Python 3) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 3 version of the package. 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: python3-w3lib Source: python-w3lib Version: 1.11.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4 Depends: neurodebian-popularity-contest, python3-six (>= 1.6.1) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python3-w3lib_1.11.0-1~nd70+1_all.deb Size: 3162 SHA256: 5ccdebd7e96456781e94b4d02fc309e93e13a3d3d9fa4023e945e662e27fa785 SHA1: b76868afdcd8740fb74c2099a3e5e6086bf78b0e MD5sum: 63d2face39f9da53a28d8702fc587f25 Description: Collection of web-related functions for Python (Python 3) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 3 version of the package. 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: spyder Version: 2.2.5+dfsg-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 113 Depends: neurodebian-popularity-contest, python, python-spyderlib (= 2.2.5+dfsg-1~nd70+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd70+1_all.deb Size: 56614 SHA256: fe5146cc4279f0846d8b9c0815bdbbda5bdda7ad8a664d81933772b465298ec4 SHA1: 61ba32cf4dbeb6bcc5e708e917e344a913401bd4 MD5sum: 52db6267791f2eee2221b83ac91a1e29 Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: spykeviewer Version: 0.4.4-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2011 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), 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_0.4.4-1~nd70+1_all.deb Size: 1344274 SHA256: f113a77bcf0c3e0b0adffe628a5cc3cfd8fee3909a27ca4cbac8f4f839dc3cbd SHA1: f698246fd9c102be479559472d55af52f810513e MD5sum: 061cfffc25feb42cc402a44da792cdac 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: 7.3-1~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_7.3-1~nd70+1_all.deb Size: 50202380 SHA256: 213cdcb76214686a0a8983ea5fc9000f30097ad7ece36f32986f81828614bbfd SHA1: 82dace7aad70a98fdf52f35495b021f2ce9caea5 MD5sum: 5cd618999611d5d61901a70a6057e23c 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).